15.3.28.2 Railroads, Inspection, Obstacles

Chapter Contents (Back)
Obstacle Detection. Collision Detection. Railroads. Inspection, Railroads. Trains.
See also Obstacle Dectection, Objects on the Road.
See also Rail Traffic Controls, Trains.

Mermec Group,
2010.
WWW Link. Vendor, Railroad Inspection.

Trosino, M.[Michael], Cunningham, J.J.[John J.], Shaw, III, A.E.[Alfred E.],
Automated track inspection vehicle and method,
US_Patent6,064,428, May 16, 2000
WWW Link. BibRef 0005
And: US_Patent6,356,299, Mar 12, 2002
WWW Link. BibRef

Mandriota, C., Nitti, M., Ancona, N., Stella, E., Distante, A.,
Filter-based feature selection for rail defect detection,
MVA(15), No. 4, October 2004, pp. 179-185.
Springer DOI 0410
BibRef

Mandriota, C., Stella, E., Nitti, M., Ancona, N., Distante, A.,
Rail Corrugation Detection by Gabor Filtering,
ICIP01(II: 626-628).
IEEE DOI 0108
BibRef

Labarile, A., Stella, E., Ancona, N., Distante, A.,
Ballast 3D reconstruction by a matching pursuit based stereo matcher,
IVS04(653-657).
IEEE DOI 0411
BibRef

Mazzeo, P.L., Nitti, M., Stella, E., Distante, A.,
Visual recognition of fastening bolts for railroad maintenance,
PRL(25), No. 6, 19 April 2004, pp. 669-677.
Elsevier DOI 0405
BibRef

Kim, Z.[ZuWhan], Cohn, T.E.,
Pseudoreal-time activity detection for railroad grade-crossing safety,
ITS(5), No. 4, December 2004, pp. 319-324.
IEEE Abstract. 0501
BibRef

Marino, F.[Francescomaria], Distante, A.[Arcangelo], Mazzeo, P.L.[Pier Luigi], Stella, E.[Ettore],
A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection,
SMC-C(37), No. 3, May 2007, pp. 418-428.
IEEE DOI 0704
BibRef

Tsang, C.W., Ho, T.K.,
Optimal Track Access Rights Allocation for Agent Negotiation in an Open Railway Market,
ITS(9), No. 1, March 2008, pp. 68-82.
IEEE DOI 0803
BibRef

Garcia, J.J., Ureña, J., Hernandez, A., Mazo, M., Jimenez, J.A., Alvarez, F.J., de Marziani, C., Jimenez, A., Diaz, M.J., Losada, C., Garcia, E.,
Efficient Multisensory Barrier for Obstacle Detection on Railways,
ITS(11), No. 3, September 2010, pp. 702-713.
IEEE DOI 1003
BibRef

Wilson, A.[Andrew],
Riding the Rails,
VisSys(16), No. 1, January 2011. Survey, Rail Inspection. Railway tunnels, bridges, and underpasses can be imaged at high speeds using linescan and area-array cameras. BibRef 1101

Hensel, S., Hasberg, C., Stiller, C.,
Probabilistic Rail Vehicle Localization With Eddy Current Sensors in Topological Maps,
ITS(12), No. 4, December 2011, pp. 1525-1536.
IEEE DOI 1112
BibRef

Huang, Y.P.[Ya-Ping], Luo, S.W.[Si-Wei], Wang, S.C.[Sheng-Chun],
Combining Boundary and Region Information with Bolt Prior for Rail Surface Detection,
IEICE(E95-D), No. 2, February 2012, pp. 690-693.
WWW Link. 1202
BibRef

Hernandez, A., Perez, M.C., Garcia, J.J., Jimenez, A., Garcia, J.C., Espinosa, F., Mazo, M., Urena, J.,
FPGA-Based Track Circuit for Railways Using Transmission Encoding,
ITS(13), No. 2, June 2012, pp. 437-448.
IEEE DOI 1206
BibRef

Li, Q.Y.[Qing-Yong], Huang, Y.P.[Ya-Ping], Liang, Z.P.[Zheng-Ping], Luo, S.W.[Si-Wei],
Thresholding Based on Maximum Weighted Object Correlation for Rail Defect Detection,
IEICE(E95-D), No. 7, July 2012, pp. 1819-1822.
WWW Link. 1208
BibRef

Xu, Z., Wang, W., Sun, Y.,
Performance Degradation Monitoring for Onboard Speed Sensors of Trains,
ITS(13), No. 3, September 2012, pp. 1287-1297.
IEEE DOI 1209
BibRef

Nassu, B.T., Ukai, M.,
A Vision-Based Approach for Rail Extraction and its Application in a Camera Pan-Tilt Control System,
ITS(13), No. 4, December 2012, pp. 1763-1771.
IEEE DOI 1212
BibRef

Zhang, Y.X.[Yi-Xin], Wang, S.[Shun], Zhang, X.P.[Xu-Ping], Xie, F.[Fei], Wang, J.[Jiaqi],
Freight train gauge-exceeding detection based on three-dimensional stereo vision measurement,
MVA(24), No. 3, April 2013, pp. 461-475.
WWW Link. 1303
BibRef

Shafiullah, G.M., Azad, S.A., Ali, A.B.M.S.,
Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications,
ITS(14), No. 2, 2013, pp. 649-659.
IEEE DOI 1307
Rail transportation; Energy efficiency BibRef

Chen, X.X.[Xiang-Xian], Zhou, G.S.[Gong-Shuang], Yang, Y.[Yi], Huang, H.[Hai],
A Newly Developed Safety-Critical Computer System for China Metro,
ITS(14), No. 2, 2013, pp. 709-719.
IEEE DOI 1307
railways; really the computer system, not vision. BibRef

Resendiz, E., Hart, J.M., Ahuja, N.,
Automated Visual Inspection of Railroad Tracks,
ITS(14), No. 2, 2013, pp. 751-760.
IEEE DOI 1307
automated visual inspection; spectral estimation method BibRef

Dong, H.R.[Hai-Rong], Ning, B.[Bin], Chen, Y.[Yao], Sun, X.B.[Xu-Bin], Wen, D.[Ding], Hu, Y.L.[Yu-Ling], Ouyang, R.[Renhai],
Emergency Management of Urban Rail Transportation Based on Parallel Systems,
ITS(14), No. 2, 2013, pp. 627-636.
IEEE DOI 1307
Rail transportation BibRef

Zhou, M.[Min], Dong, H.R.[Hai-Rong], Ning, B.[Bin], Wang, F.Y.[Fei-Yue],
Parallel Urban Rail Transit Stations for Passenger Emergency Management,
IEEE_Int_Sys(35), No. 6, November 2020, pp. 16-27.
IEEE DOI 2012
Emergency services, Rails, Optimization, Intelligent systems, Agent-based modeling, Parallel system, ACP Method, Passenger, Emergency management BibRef

Wang, H., Schmid, F., Chen, L., Roberts, C., Xu, T.,
A Topology-Based Model for Railway Train Control Systems,
ITS(14), No. 2, 2013, pp. 819-827.
IEEE DOI 1307
signaling; Safety BibRef

Wang, J., Wang, J., Roberts, C., Chen, L.,
Parallel Monitoring for the Next Generation of Train Control Systems,
ITS(16), No. 1, February 2015, pp. 330-338.
IEEE DOI 1502
Communication system signaling BibRef

Wang, H., Yu, F.R., Zhu, L., Tang, T., Ning, B.,
Finite-State Markov Modeling for Wireless Channels in Tunnel Communication-Based Train Control Systems,
ITS(15), No. 3, June 2014, pp. 1083-1090.
IEEE DOI 1407
Antenna measurements BibRef

Sohn, K.[Keemin],
Optimizing Train-Stop Positions Along a Platform to Distribute the Passenger Load More Evenly Across Individual Cars,
ITS(14), No. 2, 2013, pp. 994-1002.
IEEE DOI 1307
genetic algorithms; crowding; public transport; train-stop location BibRef

Chen, D., Chen, R., Li, Y., Tang, T.,
Online Learning Algorithms for Train Automatic Stop Control Using Precise Location Data of Balises,
ITS(14), No. 3, 2013, pp. 1526-1535.
IEEE DOI 1309
Balise BibRef

Li, L., Dong, W., Ji, Y., Zhang, Z., Tong, L.,
Minimal-Energy Driving Strategy for High-Speed Electric Train With Hybrid System Model,
ITS(14), No. 4, 2013, pp. 1642-1653.
IEEE DOI 1312
Cost function BibRef

Zhang, L., Zhuan, X.,
Braking-Penalized Receding Horizon Control of Heavy-Haul Trains,
ITS(14), No. 4, 2013, pp. 1620-1628.
IEEE DOI 1312
Computational modeling BibRef

Zhao, L.H.[Lin-Hai], Cai, B.G.[Bai-Gen], Xu, J.J.[Jun-Jie], Ran, Y.K.[Yi-Kui],
Study of the Track-Train Continuous Information Transmission Process in a High-Speed Railway,
ITS(15), No. 1, February 2014, pp. 112-121.
IEEE DOI 1403
rail traffic control BibRef

Li, Y.[Ying], Trinh, H.[Hoang], Haas, N.[Norman], Otto, C.[Charles], Pankanti, S.[Sharath],
Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection,
ITS(15), No. 2, April 2014, pp. 760-770.
IEEE DOI 1404
BibRef
Earlier: A2, A3, A1, A4, A5:
Enhanced rail component detection and consolidation for rail track inspection,
WACV12(289-295).
IEEE DOI 1203
Cameras BibRef

Trinh, H.[Hoang], Haas, N.[Norman], Pankanti, S.[Sharath],
Multisensor evidence integration and optimization in rail inspection,
ICPR12(886-889).
WWW Link. 1302
BibRef

Li, Y.[Ying], Otto, C.[Charles], Haas, N.[Norm], Fujiki, Y.C.[Yui-Chi], Pankanti, S.[Sharath],
Component-based track inspection using machine-vision technology,
ICMR11(60).
DOI Link 1301
inspection and condition monitoring of railroad tracks. BibRef

Song, Y., Song, Q., Cai, W.,
Fault-Tolerant Adaptive Control of High-Speed Trains Under Traction/Braking Failures: A Virtual Parameter-Based Approach,
ITS(15), No. 2, April 2014, pp. 737-748.
IEEE DOI 1404
Aerodynamics BibRef

Wang, Y., Song, Y., Gao, H., Lewis, F.L.,
Distributed Fault-Tolerant Control of Virtually and Physically Interconnected Systems With Application to High-Speed Trains Under Traction/Braking Failures,
ITS(17), No. 2, February 2016, pp. 535-545.
IEEE DOI 1602
Couplings BibRef

Liu, Z.[Zhen], Li, F.J.[Feng-Jiao], Huang, B.K.[Bang-Kui], Zhang, G.J.[Guang-Jun],
Real-time and accurate rail wear measurement method and experimental analysis,
JOSA-A(31), No. 8, August 2014, pp. 1721-1729.
DOI Link 1408
Optical instruments; Imaging systems; Machine vision optics BibRef

Molodova, M., Li, Z.L.[Zi-Li], Nunez, A., Dollevoet, R.,
Automatic Detection of Squats in Railway Infrastructure,
ITS(15), No. 5, October 2014, pp. 1980-1990.
IEEE DOI 1410
acceleration measurement BibRef

Ai, B.[Bo], Cheng, X.[Xiang], Kurner, T., Zhong, Z.D.[Zhang-Dui], Guan, K.[Ke], He, R.S.[Rui-Si], Xiong, L.[Lei], Matolak, D.W., Michelson, D.G., Briso-Rodriguez, C.,
Challenges Toward Wireless Communications for High-Speed Railway,
ITS(15), No. 5, October 2014, pp. 2143-2158.
IEEE DOI 1410
data communication BibRef

Kecman, P., Goverde, R.M.P.,
Online Data-Driven Adaptive Prediction of Train Event Times,
ITS(16), No. 1, February 2015, pp. 465-474.
IEEE DOI 1502
Adaptation models BibRef

Hung, R.[Raymond], King, B.[Bruce], Chen, W.[Wu],
Conceptual Issues Regarding the Development of Underground Railway Laser Scanning Systems,
IJGI(4), No. 1, 2015, pp. 185-198.
DOI Link 1502
BibRef

Salmane, H., Khoudour, L., Ruichek, Y.[Yassine],
A Video-Analysis-Based Railway-Road Safety System for Detecting Hazard Situations at Level Crossings,
ITS(16), No. 2, April 2015, pp. 596-609.
IEEE DOI 1504
Accidents BibRef

Xu, P., Liu, R., Sun, Q., Jiang, L.,
Dynamic-Time-Warping-Based Measurement Data Alignment Model for Condition-Based Railroad Track Maintenance,
ITS(16), No. 2, April 2015, pp. 799-812.
IEEE DOI 1504
Data models BibRef

Lauer, M., Stein, D.,
A Train Localization Algorithm for Train Protection Systems of the Future,
ITS(16), No. 2, April 2015, pp. 970-979.
IEEE DOI 1504
Global Positioning System BibRef

Lu, D., Schnieder, E.,
Performance Evaluation of GNSS for Train Localization,
ITS(16), No. 2, April 2015, pp. 1054-1059.
IEEE DOI 1504
Accuracy BibRef

Hodge, V.J., O'Keefe, S., Weeks, M., Moulds, A.,
Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey,
ITS(16), No. 3, June 2015, pp. 1088-1106.
IEEE DOI 1506
Sardis Award, Survey. Base stations BibRef

Aytekin, C., Rezaeitabar, Y., Dogru, S., Ulusoy, I.,
Railway Fastener Inspection by Real-Time Machine Vision,
SMCS(45), No. 7, July 2015, pp. 1101-1107.
IEEE DOI 1506
Fasteners BibRef

Arastounia, M.[Mostafa],
Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data,
RS(7), No. 11, 2015, pp. 14916.
DOI Link 1512
BibRef

Wu, X.[Xiao], Yuan, P.[Ping], Peng, Q.A.[Qi-Ang], Ngo, C.W.[Chong-Wah], He, J.Y.[Jun-Yan],
Detection of bird nests in overhead catenary system images for high-speed rail,
PR(51), No. 1, 2016, pp. 242-254.
Elsevier DOI 1601
Bird nest detection BibRef

Liu, L., Zhou, F., He, Y.,
Vision-based fault inspection of small mechanical components for train safety,
IET-ITS(10), No. 2, 2016, pp. 130-139.
DOI Link 1602
automatic optical inspection BibRef

Hyde, P., Defossez, F., Ulianov, C.,
Development and testing of an automatic remote condition monitoring system for train wheels,
IET-ITS(10), No. 1, 2016, pp. 32-40.
DOI Link 1602
computational geometry BibRef

Rama, D., Andrews, J.D.,
Railway infrastructure asset management: The whole-system life cost analysis,
IET-ITS(10), No. 1, 2016, pp. 58-64.
DOI Link 1602
Monte Carlo methods BibRef

Gibert, X.[Xavier], Patel, V.M.[Vishal M.], Chellappa, R.[Rama],
Deep Multitask Learning for Railway Track Inspection,
ITS(18), No. 1, January 2017, pp. 153-164.
IEEE DOI 1701
BibRef
Earlier:
Robust Fastener Detection for Autonomous Visual Railway Track Inspection,
WACV15(694-701)
IEEE DOI 1503
BibRef
Earlier:
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection,
CVRoads15(131-138)
IEEE DOI 1602
BibRef
Earlier:
Material Classification and Semantic Segmentation of Railway Track Images with Deep Convolutional Neural Networks,
ICIP15(621-625)
IEEE DOI 1512
Detectors. Bayes methods. Deep Convolutional Neural Networks BibRef

Espinosa, F., Hernández, Á., Mazo, M., Ureña, J., Pérez, M.C., Jiménez, J.A., Fernández, I., García, J.C., García, J.J.,
Detector of Electrical Discontinuity of Rails in Double-Track Railway Lines: Electronic System and Measurement Methodology,
ITS(18), No. 4, April 2017, pp. 743-755.
IEEE DOI 1704
Degradation BibRef

Benedetto, F., Tosti, F., Alani, A.M.,
An Entropy-Based Analysis of GPR Data for the Assessment of Railway Ballast Conditions,
GeoRS(55), No. 7, July 2017, pp. 3900-3908.
IEEE DOI 1706
Electronic ballasts, Entropy, Ground penetrating radar, Radar tracking, Rail transportation, Rails, Receiving antennas, Ballast fouling, entropy, ground penetrating radar (GPR), performance analysis, railway, ballast BibRef

Wang, F., Xu, T., Tang, T., Zhou, M., Wang, H.,
Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems,
ITS(18), No. 1, January 2017, pp. 49-58.
IEEE DOI 1701
Fault diagnosis BibRef

Dai, P.[Peng], Wang, S.C.[Sheng-Chun], Huang, Y.P.[Ya-Ping], Wang, H.[Hao], Du, X.Y.[Xin-Yu], Han, Q.A.[Qi-Ang],
Visual Indexing of Large Scale Train-Borne Video for Rail Condition Perceiving,
IEICE(E100-D), No. 9, September 2017, pp. 2017-2026.
WWW Link. 1709
BibRef

Mao, Z., Tao, G., Jiang, B., Yan, X.G.,
Adaptive Compensation of Traction System Actuator Failures for High-Speed Trains,
ITS(18), No. 11, November 2017, pp. 2950-2963.
IEEE DOI 1711
Actuators, Adaptation models, Adaptive systems, Dynamics, Force, Mathematical model, Resistance, Actuator failures, adaptive control, failure compensation, high-speed train BibRef

Gao, M., Wang, P., Wang, Y., Yao, L.,
Self-Powered ZigBee Wireless Sensor Nodes for Railway Condition Monitoring,
ITS(19), No. 3, March 2018, pp. 900-909.
IEEE DOI 1804
Magnetic levitation, Monitoring, Rail transportation, Rails, Wireless communication, Wireless sensor networks, ZigBee, wireless sensor networks BibRef

Krummenacher, G., Ong, C.S., Koller, S., Kobayashi, S., Buhmann, J.M.,
Wheel Defect Detection With Machine Learning,
ITS(19), No. 4, April 2018, pp. 1176-1187.
IEEE DOI 1804
Force measurement, Learning systems, Rail transportation, Strain measurement, Vibrations, Wavelet transforms, Wheels, wavelet transforms BibRef

Fontul, S.[Simona], Paixão, A.[André], Solla, M.[Mercedes], Pajewski, L.[Lara],
Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Fan, H., Cosman, P.C., Hou, Y., Li, B.,
High-Speed Railway Fastener Detection Based on a Line Local Binary Pattern,
SPLetters(25), No. 6, June 2018, pp. 788-792.
IEEE DOI 1806
fasteners, feature extraction, image classification, image sensors, image texture, object detection, railways, center point, visual inspection BibRef

Zhou, F.Q.A.[Fu-Qi-Ang], Song, Y.[Ya], Liu, L.[Liu], Zheng, D.T.[Dong-Tian],
Automated visual inspection of target parts for train safety based on deep learning,
IET-ITS(12), No. 6, August 2018, pp. 550-555.
DOI Link 1807
BibRef

Mao, Q.Z.[Qing-Zhou], Cui, H.[Hao], Hu, Q.W.[Qing-Wu], Ren, X.C.[Xiao-Chun],
A rigorous fastener inspection approach for high-speed railway from structured light sensors,
PandRS(143), 2018, pp. 249-267.
Elsevier DOI 1808
High-speed railway, Fastener inspection, Structured light sensor, Dense point cloud, Decision tree, Centerline extraction BibRef

Wang, H.F.[Hai-Feng], Zhao, N.[Ning], Ning, B.[Bin], Tang, T.[Tao], Chai, M.[Ming],
Safety monitor for train-centric CBTC system,
IET-ITS(12), No. 8, October 2018, pp. 931-938.
DOI Link 1809
BibRef

Lou, Y.D.[Yi-Dong], Zhang, T.[Tian], Tang, J.[Jian], Song, W.W.[Wei-Wei], Zhang, Y.[Yi], Chen, L.[Liang],
A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Chen, H., Jiang, B., Lu, N.,
A Newly Robust Fault Detection and Diagnosis Method for High-Speed Trains,
ITS(20), No. 6, June 2019, pp. 2198-2208.
IEEE DOI 1906
Principal component analysis, Fault detection, Robustness, Sensors, Aging, Probability density function, Sensitivity, Incipient faults, high-speed trains BibRef

Liu, H., Chang, Y., Li, Z., Zhong, S., Yan, L.,
Directional-Aware Automatic Defect Detection in High-Speed Railway Catenary System,
ICIP19(3930-3934)
IEEE DOI 1910
directional-aware, attention, defect detection, dropper, catenary system BibRef

Ciampoli, L.B.[Luca Bianchini], Calvi, A.[Alessandro], d'Amico, F.[Fabrizio],
Railway Ballast Monitoring by GPR: A Test-Site Investigation,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Wu, Y., Jiang, B., Lu, N.,
A Descriptor System Approach for Estimation of Incipient Faults With Application to High-Speed Railway Traction Devices,
SMCS(49), No. 10, October 2019, pp. 2108-2118.
IEEE DOI 1909
Actuators, Iron, Observers, Rail transportation, Barium, Noise measurement, Descriptor systems, state/noise estimation BibRef

Hu, F.M.[Feng-Ming], van Leijen, F.J.[Freek J.], Chang, L.[Ling], Wu, J.[Jicang], Hanssen, R.F.[Ramon F.],
Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Kano, G.[Guilherme], Andrade, T.[Tiago], Moutinho, A.[Alexandra],
Automatic Detection of Obstacles in Railway Tracks Using Monocular Camera,
CVS19(284-294).
Springer DOI 1912
BibRef

Wen, T., Dong, D., Chen, Q., Chen, L., Roberts, C.,
Maximal Information Coefficient-Based Two-Stage Feature Selection Method for Railway Condition Monitoring,
ITS(20), No. 7, July 2019, pp. 2681-2690.
IEEE DOI 1907
Feature extraction, Microwave integrated circuits, Wavelet analysis, Correlation, Wavelet packets, bearing fault BibRef

Zou, R.[Rong], Fan, X.Y.[Xiao-Yun], Qian, C.[Chuang], Ye, W.F.[Wen-Fang], Zhao, P.[Peng], Tang, J.[Jian], Liu, H.[Hui],
An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Chen, H., Jiang, B.,
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains,
ITS(21), No. 2, February 2020, pp. 450-465.
IEEE DOI 2002
Circuit faults, Fault detection, Mathematical model, Analytical models, Transportation, Temperature sensors, high-speed trains BibRef

Zhang, Z., He, X., Yuan, H.,
A Robust Parking Detection Algorithm Against Electric Railway Magnetic Field Interference,
ITS(21), No. 2, February 2020, pp. 882-893.
IEEE DOI 2002
Interference, Rail transportation, Magnetic sensors, Magnetometers, Perpendicular magnetic anisotropy, Space vehicles, morphological filter BibRef

Shankar, S.[Sangeetha], Roth, M.[Michael], Schubert, L.A.[Lucas Andreas], Verstegen, J.A.[Judith Anne],
Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Wei, X., Jiang, S., Li, Y., Li, C., Jia, L., Li, Y.,
Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology,
ITS(21), No. 3, March 2020, pp. 947-958.
IEEE DOI 2003
Rail transportation, Image edge detection, Head, Deep learning, Picture archiving and communication systems, Inspection, railway BibRef

Li, Y., Zhong, X., Ma, Z., Liu, H.,
The Outlier and Integrity Detection of Rail Profile Based on Profile Registration,
ITS(21), No. 3, March 2020, pp. 1074-1085.
IEEE DOI 2003
Rails, Standards, Measurement by laser beam, Data models, Anomaly detection, rail wear measurement BibRef

Ning, X.W.[Xin-Wen], Zhu, Q.[Qing], Zhang, H.[Heng], Wang, C.J.[Chang-Jin], Han, Z.[Zujie], Zhang, J.X.[Jun-Xiao], Zhao, W.[Wen],
Dynamic Simulation Method of High-Speed Railway Engineering Construction Processes Based on Virtual Geographic Environment,
IJGI(9), No. 5, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Hong, N.[Ning], Li, L.S.[Li-Shuai], Yao, W.R.[Wei-Ran], Zhao, Y.[Yang], Yi, C.[Cai], Lin, J.H.[Jian-Hui], Tsui, K.L.[Kwok Leung],
High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data,
ITS(21), No. 7, July 2020, pp. 2943-2955.
IEEE DOI 2007
BibRef
And: Correction: ITS(22), No. 9, September 2021, pp. 6088-6088.
IEEE DOI 2109
Monitoring, Data models, Suspensions (mechanical systems), Vibrations, Feature extraction, Vehicle dynamics, data-driven approach BibRef

Wang, H., Núñez, A., Liu, Z., Zhang, D., Dollevoet, R.,
A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries,
ITS(21), No. 10, October 2020, pp. 4037-4051.
IEEE DOI 2010
Condition monitoring, Rail transportation, Inspection, Wires, Bayes methods, Dynamics, High-speed railway, catenary, key performance indicator BibRef

Ye, J.Q.[Jia-Qi], Stewart, E.[Edward], Zhang, D.C.[Ding-Cheng], Chen, Q.Y.[Qian-Yu], Roberts, C.[Clive],
Method for automatic railway track surface defect classification and evaluation using a laser-based 3D model,
IET-IPR(14), No. 12, October 2020, pp. 2701-2710.
DOI Link 2010
BibRef

Liu, J.W.[Jian-Wei], Liu, H.L.[Hong-Li], Ni, X.F.[Xue-Feng], Ma, Z.J.[Zi-Ji], Shao, C.W.X.[Chao Wang Xun],
A Visual Inspection System for Accurate Positioning of Railway Fastener,
IEICE(E103-D), No. 10, October 2020, pp. 2208-2215.
WWW Link. 2010
BibRef

Karunathilake, A.[Amila], Honma, R.[Ryohei], Niina, Y.[Yasuhito],
Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Velha, P., Nannipieri, T., Signorini, A., Morosi, M., Solazzi, M., Barone, F., Frisoli, A., Ricciardi, L., Eusepi, R., Icardi, M., Recchia, G., Lupi, M., Arcoleo, G., Firmi, P., di Pasquale, F.,
Monitoring Large Railways Infrastructures Using Hybrid Optical Fibers Sensor Systems,
ITS(21), No. 12, December 2020, pp. 5177-5188.
IEEE DOI 2012
Strain, Fiber gratings, Temperature sensors, Optical fiber sensors, Bragg grating, finite element analysis, optical fiber sensors, Raman scattering BibRef

Samra, M., Chen, L., Roberts, C., Constantinou, C., Shukla, A.,
TV White Spaces Handover Scheme for Enabling Unattended Track Geometry Monitoring From In-Service Trains,
ITS(22), No. 2, February 2021, pp. 1161-1173.
IEEE DOI 2102
TV, Handover, Rail transportation, Databases, Geometry, Monitoring, Dynamic spectrum access, railway communications, TV white spaces BibRef

Ghassoun, Y.[Yahya], Gerke, M.[Markus], Khedar, Y.[Yogesh], Backhaus, J.[Jan], Bobbe, M.[Markus], Meissner, H.[Henry], Tiwary, P.K.[Prashant Kumar], Heyen, R.[Ralf],
Implementation and Validation of a High Accuracy UAV-Photogrammetry Based Rail Track Inspection System,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Ye, T.[Tao], Zhang, X.[Xi], Zhang, Y.[Yi], Liu, J.[Jie],
Railway Traffic Object Detection Using Differential Feature Fusion Convolution Neural Network,
ITS(22), No. 3, March 2021, pp. 1375-1387.
IEEE DOI 2103
Rail transportation, Feature extraction, Object detection, Safety, Radar tracking, Real-time systems, Detectors, prior module BibRef

Jeansoulin, R.[Robert],
A Century of French Railways: The Value of Remote Sensing and VGI in the Fusion of Historical Data,
IJGI(10), No. 3, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Kucera, M.[Michal], Dobesova, Z.[Zdena],
Analysis of the Degree of Threat to Railway Infrastructure by Falling Tree Vegetation,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Rekavandi, A.M.[Aref Miri], Seghouane, A.K.[Abd-Krim], Evans, R.J.[Robin J.],
Robust Subspace Detectors Based on a-Divergence With Application to Detection in Imaging,
IP(30), 2021, pp. 5017-5031.
IEEE DOI 2106
Detectors, Maximum likelihood estimation, Robustness, Light rail systems, Pollution measurement, Gaussian noise, Rao test BibRef

Pan, X.[Xiao], Liu, Z.[Zhen], Zhang, G.J.[Guang-Jun],
Line Structured-Light Vision Sensor Calibration Based on Multi-Tooth Free-Moving Target and Its Application in Railway Fields,
ITS(22), No. 9, September 2021, pp. 5762-5771.
IEEE DOI 2109
Calibration, Vision sensors, Rail transportation, Lasers, Cameras, Feature extraction, Calibration, uncertainty BibRef

Furitsu, Y.[Yuki], Deguchi, D.[Daisuke], Kawanishi, Y.[Yasutomo], Ide, I.[Ichiro], Murase, H.[Hiroshi], Mukojima, H.[Hiroki], Nagamine, N.[Nozomi],
Soft-Boundary Label Relaxation with class placement constraints for semantic segmentation of the railway environment,
PRL(150), 2021, pp. 258-264.
Elsevier DOI 2109
Semantic segmentation, Railway, Label relaxation BibRef

Liu, S.P.[Si-Ping], Tu, X.H.[Xiao-Han], Xu, C.[Cheng], Chen, L.P.[Li-Pei], Lin, S.[Shuai], Li, R.[Renfa],
An Optimized Deep Neural Network for Overhead Contact System Recognition from LiDAR Point Clouds,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
maintain the safety of railway systems BibRef

Zauner, G.[Gerald], Groessbacher, D.[David], Buerger, M.[Martin], Auer, F.[Florian], Staccone, G.[Giuseppe],
Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Wang, C.[Chao], Zeng, J.[Jiuzhen],
Combination-Chord Measurement of Rail Corrugation Using Triple-Line Structured-Light Vision: Rectification and Optimization,
ITS(22), No. 11, November 2021, pp. 7256-7265.
IEEE DOI 2112
Rails, Optimization, Vibrations, Cameras, Wavelength measurement, Sensors, Laser modes, Combination-chord model, rail corrugation, optimization BibRef

Xu, L.[Lei], Zheng, S.[Shunyi], Na, J.M.[Jia-Ming], Yang, Y.W.[Yuan-Wei], Mu, C.L.[Chun-Lin], Shi, D.B.[De-Bin],
A Vehicle-Borne Mobile Mapping System Based Framework for Semantic Segmentation and Modeling on Overhead Catenary System Using Deep Learning,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
Railway inspection. BibRef

Cheng, C.[Chao], Wang, J.[Jiuhe], Zhou, Z.J.[Zhi-Jie], Teng, W.X.[Wan-Xiu], Sun, Z.B.[Zhong-Bo], Zhang, B.C.[Bang-Cheng],
A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems,
ITS(23), No. 1, January 2022, pp. 110-121.
IEEE DOI 2201
Gears, Fault diagnosis, Monitoring, Sensors, Interference, Data models, Fault diagnosis, belief rule base, mixed reliability, high-speed trains BibRef

Lin, S.[Sheng], Fan, R.D.[Rui-Dong], Feng, D.[Ding], Yang, C.[Chao], Wang, Q.[Qi], Gao, S.B.[Shi-Bin],
Condition-Based Maintenance for Traction Power Supply Equipment Based on Partially Observable Markov Decision Process,
ITS(23), No. 1, January 2022, pp. 175-189.
IEEE DOI 2201
Maintenance engineering, Markov processes, Degradation, Uncertainty, Decision making, Traction power supplies, Reliability, traction power supply equipment BibRef

Gao, S.B.[Shi-Bin], Kang, G.Q.[Gao-Qiang], Yu, L.[Long], Zhang, D.K.[Dong-Kai], Wei, X.G.[Xiao-Guang], Zhan, D.[Dong],
Adaptive Deep Learning for High-Speed Railway Catenary Swivel Clevis Defects Detection,
ITS(23), No. 2, February 2022, pp. 1299-1310.
IEEE DOI 2202
Adaptation models, Uncertainty, Adaptive systems, Inspection, Rail transportation, Training, Feature extraction, uncertainty estimation BibRef

Park, J.[Jaegeun], Lee, B.H.[Byung-Hun], Eun, Y.[Yongsoon],
Virtual Coupling of Railway Vehicles: Gap Reference for Merge and Separation, Robust Control, and Position Measurement,
ITS(23), No. 2, February 2022, pp. 1085-1096.
IEEE DOI 2202
Couplings, Acceleration, Rail transportation, Uncertainty, Resistance, Error correction, Force, Sliding model control, railway balise BibRef

Ma, Z.J.[Zi-Ji], Xu, K.H.[Ke-Huang], Teng, Y.[Yun], Shao, X.[Xun], Dong, M.X.[Mian-Xiong], Wang, Y.[Yaonan],
A Model of Extraction of Rail's Vertical Corrugation Based on Flexible Virtual Ruler,
ITS(23), No. 2, February 2022, pp. 1097-1108.
IEEE DOI 2202
Rails, Standards, Extraterrestrial measurements, Mathematical model, Current measurement, Wavelength measurement, successive approximation BibRef

Sang, K.[Kun], Fontana, G.L.[Giovanni Luigi], Piovan, S.E.[Silvia Elena],
Assessing Railway Landscape by AHP Process with GIS: A Study of the Yunnan-Vietnam Railway,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Chen, C.[Cen], Zou, X.F.[Xiao-Feng], Zeng, Z.[Zeng], Cheng, Z.Y.[Zhong-Yao], Zhang, L.[Le], Hoi, S.C.H.[Steven C. H.],
Exploring Structural Knowledge for Automated Visual Inspection of Moving Trains,
Cyber(52), No. 2, February 2022, pp. 1233-1246.
IEEE DOI 2202
Object detection, Visualization, Fasteners, Feature extraction, Inspection, Proposals, Wheels, Automated visual inspection, train component detection BibRef

Chen, H.T.[Hong-Tian], Jiang, B.[Bin], Ding, S.X.[Steven X.], Huang, B.[Biao],
Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives,
ITS(23), No. 3, March 2022, pp. 1700-1716.
IEEE DOI 2203
Mathematical model, Sensor systems, Traction motors, Reliability, Intelligent transportation systems, Safety, Data-driven, high-speed trains BibRef

Mao, Z.[Zehui], Xia, M.X.[Ming-Xuan], Jiang, B.[Bin], Xu, D.Z.[De-Zhi], Shi, P.[Peng],
Incipient Fault Diagnosis for High-Speed Train Traction Systems via Stacked Generalization,
Cyber(52), No. 8, August 2022, pp. 7624-7633.
IEEE DOI 2208
Fault diagnosis, Circuit faults, Stacking, Sensors, Sensor systems, Mathematical model, Radio frequency, Fault diagnosis, stacked generalization BibRef

Xu, Y.S.[Yun-Song], Long, Z.Q.[Zhi-Qiang], Zhao, Z.G.[Zhen-Gen], Zhai, M.[Mingda], Wang, Z.Q.[Zhi-Qiang],
Real-Time Stability Performance Monitoring and Evaluation of Maglev Trains' Levitation System: A Data-Driven Approach,
ITS(23), No. 3, March 2022, pp. 1912-1923.
IEEE DOI 2203
Stability analysis, Monitoring, Magnetic levitation vehicles, Real-time systems, Performance evaluation, Real-time, magnetic levitation system BibRef

Alvarenga, T.A.[Tiago A.], Nóbrega, R.A.[Rafael A.], Cerqueira, A.S.[Augusto S.], de Andrade Filho, L.M.[Luciano M.], Honório, L.M.[Leonardo M.], Rossignoli, S.[Sérgio], Veloso, H.[Henrique], Batista, R.[Rafael],
Identification of Low Impedance Points Along Railway Tracks From a Railroad Inspection Vehicle,
ITS(23), No. 3, March 2022, pp. 1807-1817.
IEEE DOI 2203
Insulation life, Degradation, Impedance, Rail transportation, Inspection, Transmission line measurements, Rails, standing waves theory BibRef

Zhao, Y.H.[Ying-Hong], He, X.[Xiao], Zhou, D.H.[Dong-Hua], Pecht, M.G.[Michael G.],
Detection and Isolation of Wheelset Intermittent Over-Creeps for Electric Multiple Units Based on a Weighted Moving Average Technique,
ITS(23), No. 4, April 2022, pp. 3392-3405.
IEEE DOI 2204
Adhesives, Creep, Force, Torque, Indexes, Sensors, Rails, Slip and slide, detection and isolation, weighted moving average, optimal weight, electric multiple units BibRef

Chen, C.[Cen], Li, K.[Kenli], Zhong-Yao, C.[Cheng], Piccialli, F.[Francesco], Hoi, S.C.H.[Steven C. H.], Zeng, Z.[Zeng],
A Hybrid Deep Learning Based Framework for Component Defect Detection of Moving Trains,
ITS(23), No. 4, April 2022, pp. 3268-3280.
IEEE DOI 2204
Deep learning, Rail transportation, Object detection, Inspection, Semantics, Image segmentation, Feature extraction, visual inspection BibRef

Karaduman, G.[Gulsah], Akin, E.[Erhan],
A New Approach Based on Predictive Maintenance Using the Fuzzy Classifier in Pantograph-Catenary Systems,
ITS(23), No. 5, May 2022, pp. 4236-4246.
IEEE DOI 2205
Temperature sensors, Correlation, Rail transportation, Predictive maintenance, Strips, Matlab, Temperature distribution, predictive maintenance BibRef

Ni, X.F.[Xue-Feng], Liu, H.L.[Hong-Li], Ma, Z.[Ziji], Wang, C.[Chao], Liu, J.W.[Jian-Wei],
Detection for Rail Surface Defects via Partitioned Edge Feature,
ITS(23), No. 6, June 2022, pp. 5806-5822.
IEEE DOI 2206
Rails, Inspection, Surface morphology, Image edge detection, Visualization, Surface cracks, Rail transportation, rail surface defects BibRef

Liu, S.[Su], Yin, C.S.[Cheng-Shuang], Chen, D.J.[Ding-Jun], Lv, H.X.[Hong-Xia], Zhang, Q.P.[Qing-Peng],
Cascading Failure in Multiple Critical Infrastructure Interdependent Networks of Syncretic Railway System,
ITS(23), No. 6, June 2022, pp. 5740-5753.
IEEE DOI 2206
Rail transportation, Robustness, Power system protection, Power system faults, Rails, Analytical models, syncretic railway system BibRef

Xu, J.P.[Jian-Peng], Ai, B.[Bo],
Experience-Driven Power Allocation Using Multi-Agent Deep Reinforcement Learning for Millimeter-Wave High-Speed Railway Systems,
ITS(23), No. 6, June 2022, pp. 5490-5500.
IEEE DOI 2206
Resource management, Array signal processing, Rail transportation, Radio frequency, Uplink, Optimization, multi-agent deep reinforcement learning BibRef

Zhang, H.[Hui], Song, Y.[Yanan], Chen, Y.R.[Yu-Rong], Zhong, H.[Hang], Liu, L.[Li], Wang, Y.N.[Yao-Nan], Akilan, T.[Thangarajah], Wu, Q.M.J.[Q. M. Jonathan],
MRSDI-CNN: Multi-Model Rail Surface Defect Inspection System Based on Convolutional Neural Networks,
ITS(23), No. 8, August 2022, pp. 11162-11177.
IEEE DOI 2208
Rails, Surface cracks, Surface waves, Surface morphology, Feature extraction, Real-time systems, Rail transportation, one-stage BibRef

Zuo, Y.K.[Ya-Kun], Wang, N.[Ning], Jia, L.M.[Li-Min], Zhang, H.Y.[Hui-Yue], Wang, Z.P.[Zhi-Peng], Qin, Y.[Yong],
Fully Decomposed Singular Value and Fixed Dictionary Extreme Learning Machine for Bogie Fault Diagnosis,
ITS(23), No. 8, August 2022, pp. 10262-10274.
IEEE DOI 2208
Feature extraction, Fault diagnosis, Matrix decomposition, Rails, Dictionaries, Employee welfare, Vibrations, Bogie, fault diagnosis, variable conditions BibRef

Wang, X.Y.[Xin-Yue], Huang, D.Q.[De-Qing], Qin, N.[Na], Chen, C.R.[Chun-Rong], Zhang, K.[Kai],
Modeling and Second-Order Sliding Mode Control for Lateral Vibration of High-Speed Train With MR Dampers,
ITS(23), No. 8, August 2022, pp. 10299-10308.
IEEE DOI 2208
Shock absorbers, Vibrations, Force, Pistons, Damping, Mathematical model, Magnetic hysteresis, High-speed train, SOSM control BibRef

Zeng, Y.C.[Yuan-Chen], Song, D.L.[Dong-Li], Zhang, W.H.[Wei-Hua], Zhou, B.[Bin], Xie, M.Y.[Ming-Yuan], Tang, X.[Xu],
An Optimal Life Cycle Reprofiling Strategy of Train Wheels Based on Markov Decision Process of Wheel Degradation,
ITS(23), No. 8, August 2022, pp. 10354-10364.
IEEE DOI 2208
Wheels, Maintenance engineering, Degradation, Biological system modeling, Rail transportation, train wheels BibRef

Liu, J.W.[Jian-Wei], Ma, Z.[Ziji], Qiu, Y.[Yuan], Ni, X.F.[Xue-Feng], Shi, B.[Bo], Liu, H.L.[Hong-Li],
Four Discriminator Cycle-Consistent Adversarial Network for Improving Railway Defective Fastener Inspection,
ITS(23), No. 8, August 2022, pp. 10636-10645.
IEEE DOI 2208
Fasteners, Inspection, Generative adversarial networks, Image synthesis, Feature extraction, Solid modeling, deep learning BibRef

Cao, Y.[Yuan], Sun, Y.[Yongkui], Xie, G.[Guo], Li, P.[Peng],
A Sound-Based Fault Diagnosis Method for Railway Point Machines Based on Two-Stage Feature Selection Strategy and Ensemble Classifier,
ITS(23), No. 8, August 2022, pp. 12074-12083.
IEEE DOI 2208
Feature extraction, Fault diagnosis, Time-domain analysis, Rail transportation, Time-frequency analysis, ensemble classifier BibRef

Zhuang, L.[Li], Qi, H.Y.[Hao-Yang], Zhang, Z.J.[Zi-Jun],
The Automatic Rail Surface Multi-Flaw Identification Based on a Deep Learning Powered Framework,
ITS(23), No. 8, August 2022, pp. 12133-12143.
IEEE DOI 2208
Rails, Corrugated surfaces, Surface morphology, Inspection, Rail transportation, Feature extraction, Surface treatment, condition monitoring BibRef

Wang, Z.Y.[Zhang-Yu], Yu, G.Z.[Gui-Zhen], Chen, P.[Peng], Zhou, B.[Bin], Yang, S.[Songyue],
FarNet: An Attention-Aggregation Network for Long-Range Rail Track Point Cloud Segmentation,
ITS(23), No. 8, August 2022, pp. 13118-13126.
IEEE DOI 2208
Rails, Laser radar, Feature extraction, Image segmentation, Semantics, Sensors, Rail transportation, Semantic segmentation, rail track BibRef

Zhang, Y.[Yao], Cheng, Y.[Yu], Xu, T.H.[Tian-Hua], Wang, G.[Guang], Chen, C.[Cong], Yang, T.T.[Tian-Tian],
Fault Prediction of Railway Turnout Systems Based on Improved Sparse Auto Encoder and Gated Recurrent Unit Network,
ITS(23), No. 8, August 2022, pp. 12711-12723.
IEEE DOI 2208
Feature extraction, Rail transportation, Circuit faults, Degradation, Correlation, Robustness, Rails, Fault prediction, gated recurrent unit network BibRef

Tong, L.[Lei], Wang, Z.P.[Zhi-Peng], Jia, L.M.[Li-Min], Qin, Y.[Yong], Wei, Y.B.[Yan-Bin], Yang, H.Z.[Huai-Zhi], Geng, Y.X.[Yi-Xuan],
Fully Decoupled Residual ConvNet for Real-Time Railway Scene Parsing of UAV Aerial Images,
ITS(23), No. 9, September 2022, pp. 14806-14819.
IEEE DOI 2209
Rail transportation, Convolution, Correlation, Real-time systems, Inspection, Task analysis, Computer architecture, UAV BibRef

Lu, X.M.[Xue-Min], Quan, W.[Wei], Gao, S.B.[Shi-Bin], Zhang, G.X.[Guang-Xiao], Feng, K.[Kuan], Lin, G.S.[Guo-Song], Chen, J.X.[Jim X.],
A Segmentation-Based Multitask Learning Approach for Isolating Switch State Recognition in High-Speed Railway Traction Substation,
ITS(23), No. 9, September 2022, pp. 15922-15939.
IEEE DOI 2209
Switches, Rail transportation, Substations, Strips, Semantics, Switching circuits, Feature extraction, high-speed railway traction substation BibRef

Yu, H.[Hang], Lu, J.[Jie], Liu, A.[Anjin], Wang, B.[Bin], Li, R.M.[Rui-Min], Zhang, G.Q.[Guang-Quan],
Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning,
ITS(23), No. 9, September 2022, pp. 15155-15165.
IEEE DOI 2209
Real-time systems, Load modeling, Predictive models, Mars, Open data, Training data, Task analysis, Transportation systems, concept drift BibRef

Man, J.[Jie], Dong, H.H.[Hong-Hui], Jia, L.M.[Li-Min], Qin, Y.[Yong],
AttGGCN Model: A Novel Multi-Sensor Fault Diagnosis Method for High-Speed Train Bogie,
ITS(23), No. 10, October 2022, pp. 19511-19522.
IEEE DOI 2210
Sensors, Fault diagnosis, Axles, Convolution, Traction motors, Temperature sensors, Rail transportation, Fault diagnosis, high-speed train bogie BibRef

Man, J.[Jie], Dong, H.H.[Hong-Hui], Jia, L.M.[Li-Min], Qin, Y.[Yong], Zhang, J.[Jun],
An Adaptive Multisensor Fault Diagnosis Method for High-Speed Train Bogie,
ITS(24), No. 6, June 2023, pp. 6292-6306.
IEEE DOI 2306
Fault diagnosis, Feature extraction, Axles, Temperature sensors, Convolutional neural networks, Temperature measurement, Safety, high-speed train bogie BibRef

Wu, J.Y.[Jun-Yi], Zhou, W.[Wujie], Qiu, W.W.[Wei-Wei], Yu, L.[Lu],
Depth Repeated-Enhancement RGB Network for Rail Surface Defect Inspection,
SPLetters(29), 2022, pp. 2053-2057.
IEEE DOI 2210
Surface morphology, Rails, Surface treatment, Inspection, Decoding, Weight measurement, Rail flaws detection, RGB-D image, multimodality complementation BibRef

Ye, T.[Tao], Zhang, J.[Jun], Zhao, Z.Y.[Zong-Yang], Zhou, F.Q.[Fu-Qiang],
Foreign Body Detection in Rail Transit Based on a Multi-Mode Feature-Enhanced Convolutional Neural Network,
ITS(23), No. 10, October 2022, pp. 18051-18063.
IEEE DOI 2210
Rail transportation, Feature extraction, Object detection, Real-time systems, Convolution, Training, Rails, multi-mode feature enhanced convolutional neural network BibRef

Ye, T.[Tao], Zhao, Z.[Zongyang], Wang, S.[Shouan], Zhou, F.Q.[Fu-Qiang], Gao, X.Z.[Xiao-Zhi],
A Stable Lightweight and Adaptive Feature Enhanced Convolution Neural Network for Efficient Railway Transit Object Detection,
ITS(23), No. 10, October 2022, pp. 17952-17965.
IEEE DOI 2210
Feature extraction, Rail transportation, Safety, Real-time systems, Object detection, Convolution, Adaptive systems, railway safety BibRef

Liu, H.[Hao], Yao, L.[Lianbi], Xu, Z.W.[Zheng-Wen], Fan, X.Z.[Xian-Zheng], Jiao, X.[Xiongfeng], Sun, P.P.[Pan-Pan],
A Railway Lidar Point Cloud Reconstruction Based on Target Detection and Trajectory Filtering,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Zheng, Z.X.[Zhong-Xing], Liu, W.M.[Wei-Ming], Liu, R.K.[Rui-Kang], Wang, L.[Liang], Mao, L.[Liang], Qiu, Q.S.[Qi-Sheng], Ling, G.Z.[Guang-Zheng],
Anomaly Detection of Metro Station Tracks Based on Sequential Updatable Anomaly Detection Framework,
CirSysVideo(32), No. 11, November 2022, pp. 7677-7691.
IEEE DOI 2211
Anomaly detection, Image reconstruction, Task analysis, Training, Feature extraction, Data models, Videos, Railway traffic BibRef

Liu, H.M.[Hong-Ming], Duan, X.[Xiang], Jiang, J.[Jianqun], Bian, X.C.[Xue-Cheng],
Dynamic Responses of Ballastless High-Speed Railway Due to Train Passage With Excitation of Uneven Trackbed Settlement,
ITS(23), No. 11, November 2022, pp. 22244-22257.
IEEE DOI 2212
Slabs, Rail transportation, Mathematical models, Electronic ballasts, Vibrations, Vehicle dynamics, Rails, uneven settlement BibRef

Gonzalo, A.P.[Alfredo Peinado], Horridge, R.[Richard], Steele, H.[Heather], Stewart, E.[Edward], Entezami, M.[Mani],
Review of Data Analytics for Condition Monitoring of Railway Track Geometry,
ITS(23), No. 12, December 2022, pp. 22737-22754.
IEEE DOI 2212
Radar tracking, Geometry, Degradation, Maintenance engineering, Electronic ballasts, Inspection, Data analytics, track geometry BibRef

Qi, W.W.[Wei-Wei], Zheng, S.B.[Shu-Bin], Li, L.M.[Li-Ming], Yang, Z.L.[Zheng-Long],
Loosening Bolts Detection of Bogie Box in Metro Vehicles Based on Deep Learning,
IEICE(E105-D), No. 11, November 2022, pp. 1990-1993.
WWW Link. 2212
BibRef

Su, S.X.[Shi-Xiang], Du, S.[Songlin], Lu, X.B.[Xiao-Bo],
Geometric Constraint and Image Inpainting-Based Railway Track Fastener Sample Generation for Improving Defect Inspection,
ITS(23), No. 12, December 2022, pp. 23883-23895.
IEEE DOI 2212
Fasteners, Rail transportation, Inspection, Task analysis, Skeleton, Image synthesis, Training, Image generation, image inpainting, railway fastener inspection BibRef

Liu, S.[Scarlett], Li, C.[Chao], Yuwen, T.[Tian], Wan, Z.L.[Zheng-Liang], Luo, Y.P.[Yi-Ping],
A Lightweight LiDAR-Camera Sensing Method of Obstacles Detection and Classification for Autonomous Rail Rapid Transit,
ITS(23), No. 12, December 2022, pp. 23043-23058.
IEEE DOI 2212
Point cloud compression, Laser radar, Sensors, Subspace constraints, Semantics, Clustering algorithms, point cloud classification BibRef

Ji, W.J.[Wen-Jiang], Zuo, Y.[Yuan], Fei, R.[Rong], Xie, G.[Guo], Zhang, J.L.[Jiu-Long], Hei, X.H.[Xin-Hong],
An Adaptive Fault Diagnosis Model for Railway Single and Double Action Turnout,
ITS(24), No. 1, January 2023, pp. 1314-1324.
IEEE DOI 2301
Switches, Rail transportation, Fault diagnosis, Circuit faults, Heuristic algorithms, Time series analysis, Feature extraction, dynamic time warping BibRef

Su, S.X.[Shi-Xiang], Du, S.[Songlin], Wei, X.[Xuan], Lu, X.B.[Xiao-Bo],
RFS-Net: Railway Track Fastener Segmentation Network With Shape Guidance,
CirSysVideo(33), No. 3, March 2023, pp. 1398-1412.
IEEE DOI 2303
Fasteners, Shape, Streaming media, Rail transportation, Feature extraction, Image edge detection, Task analysis, shape guidance BibRef

Wang, N.[Ning], Kou, L.L.[Lin-Lin], Zhang, H.Y.[Hui-Yue], Jia, L.M.[Li-Min], Qin, Y.[Yong], Wang, H.G.[Hong-Guang], Wang, Z.P.[Zhi-Peng],
A self-adaptive phase-segmentation and health assessment framework for point machines,
IET-ITS(17), No. 4, 2023, pp. 730-743.
DOI Link 2304
Rail inspecton. aMMTS, confidence value, degradation degree, health assessment, non-linear dynamic time warping, phase segmentation, point machine BibRef

Ji, H.H.[Hong-Hai], Zhou, J.[Jinyao], Wang, L.[Li], Li, Z.X.[Zhen-Xuan], Fan, L.L.[Ling-Ling], Hou, Z.S.[Zhong-Sheng],
Adaptive Iterative Learning Kalman Consensus Filtering for High-Speed Train Identification and Estimation,
ITS(24), No. 5, May 2023, pp. 4988-5002.
IEEE DOI 2305
Estimation, Kalman filters, Resistance, Aerodynamics, Uncertainty, Parameter estimation, Nonlinear dynamical systems, Kalman consensus filters BibRef

Guo, X.X.[Xiao-Xuan], Ji, Z.[Zhenyan], Feng, Q.[Qibo], Wang, H.H.[Hui-Hui], Yang, Y.Y.[Yan-Yan], Li, Z.[Zhao],
URS: A Light-Weight Segmentation Model for Train Wheelset Monitoring,
ITS(24), No. 7, July 2023, pp. 7707-7716.
IEEE DOI 2307
Convolution, Feature extraction, Image segmentation, Decoding, Laser modes, Semantics, Data mining, Defect segmentation, multi-line laser images BibRef

Pan, W.B.[Wen-Bo], Fan, X.H.[Xiang-Hua], Li, H.B.[Hong-Bo], He, K.[Kai],
Long-Range Perception System for Road Boundaries and Objects Detection in Trains,
RS(15), No. 14, 2023, pp. 3473.
DOI Link 2307
BibRef

Yang, J.X.[Jin-Xin], Zhou, W.J.[Wu-Jie], Wu, R.M.[Rui-Ming], Fang, M.X.[Mei-Xin],
CSANet: Contour and Semantic Feature Alignment Fusion Network for Rail Surface Defect Detection,
SPLetters(30), 2023, pp. 972-976.
IEEE DOI 2308
Feature extraction, Semantics, Convolution, Surface treatment, Surface morphology, Saliency detection, Rails, rail surface defect inspection BibRef

Zhang, Z.X.[Zhao-Xiang], Zhang, L.[Limao],
Unsupervised Pixel-Level Detection of Rail Surface Defects Using Multistep Domain Adaptation,
SMCS(53), No. 9, September 2023, pp. 5784-5795.
IEEE DOI 2309
BibRef

Gomez-Jauregui, V.[Valentin], Agustín, J.[Javier], Badolato, A.[Alejandro], del-Castillo-Igareda, J.[Jesús], de Dios-Sanz-Bobi, J.[Juan], Carrera-Monterde, A.[Ana], Manchado, C.[Cristina], Otero, C.[César],
New methods and functionalities for railway maintenance through a draisine prototype based on RADAR sensors,
IET-ITS(17), No. 8, 2023, pp. 1608-1628.
DOI Link 2309
maintenance engineering, position measurement, radar tracking, rail transportation, railway engineering BibRef

Wang, Z.P.[Zhi-Peng], Geng, Y.X.[Yi-Xuan], Jia, L.M.[Li-Min], Qin, Y.[Yong], Chai, Y.Y.[Yuan-Yuan], Tong, L.[Lei], Liu, K.[Keyan],
Self-Attentive Local Aggregation Learning With Prototype Guided Regularization for Point Cloud Semantic Segmentation of High-Speed Railways,
ITS(24), No. 10, October 2023, pp. 11157-11170.
IEEE DOI 2310
BibRef

Liu, W.[Wei], Lu, X.B.[Xiao-Bo], Wei, Y.[Yun], Ran, Z.D.[Zhi-Dan],
MFANet: Multifaceted Feature Aggregation Network for Oil Stains Detection of High-Speed Trains,
ITS(24), No. 11, November 2023, pp. 12331-12344.
IEEE DOI 2311
BibRef

Cui, J.[Jing], Qin, Y.[Yong], Wu, Y.P.[Yun-Peng], Shao, C.H.[Chang-Hong], Yang, H.Z.[Huai-Zhi],
Skip Connection YOLO Architecture for Noise Barrier Defect Detection Using UAV-Based Images in High-Speed Railway,
ITS(24), No. 11, November 2023, pp. 12180-12195.
IEEE DOI 2311
BibRef

d'Amico, G.[Gianluca], Marinoni, M.[Mauro], Nesti, F.[Federico], Rossolini, G.[Giulio], Buttazzo, G.[Giorgio], Sabina, S.[Salvatore], Lauro, G.[Gianluigi],
TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation,
ITS(24), No. 12, December 2023, pp. 15006-15017.
IEEE DOI 2312
BibRef

Zhu, G.Y.[Guang-Yu], Sun, R.R.[Ran-Ran], Fan, J.X.[Jia-Xin], Li, F.[Furong], Hou, Y.H.[Yu-Hong], Yu, H.[Hui], Liu, P.X.P.[Peter Xiao-Ping],
Coupling Effect and Chain Evolution of Urban Rail Transit Emergencies,
ITS(25), No. 1, January 2024, pp. 1044-1053.
IEEE DOI 2402
Couplings, Rails, Analytical models, Bayes methods, Sun, Uncertainty, Rail transportation, Urban rail transit (URT), emergency chain, coupled map lattices BibRef

Liu, J.W.[Jian-Wei], Qiu, Y.[Yuan], Ni, X.F.[Xue-Feng], Shi, B.[Bo], Liu, H.L.[Hong-Li],
Fast Detection of Railway Fastener Using a New Lightweight Network Op-YOLOv4-Tiny,
ITS(25), No. 1, January 2024, pp. 133-143.
IEEE DOI 2402
Fasteners, Feature extraction, Real-time systems, Convolution, Task analysis, Rail transportation, Inspection, Fastener detection, YOLOv4-tiny BibRef

Gao, S.[Shuai], Song, Q.J.[Qi-Jiang], Shen, D.[Dong],
Distributed Learning Control for High-Speed Trains Subject to Operation Safety Constraints,
Cyber(54), No. 3, March 2024, pp. 1794-1805.
IEEE DOI 2402
Safety, Trajectory tracking, Resistance, Couplers, Force, Mathematical models, Task analysis, tracking control BibRef

Tang, T.J.[Tie-Jian], Cao, J.H.[Jing-Hao], Yang, X.[Xiong], Liu, S.[Sheng], Zhu, D.S.[Dong-Sheng], Du, S.[Sidan], Li, Y.[Yang],
A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing,
RS(16), No. 8, 2024, pp. 1441.
DOI Link 2405
BibRef

Kang, R.W.[Ren-Wei], Pang, Y.Z.[Yan-Zhi], Cheng, J.F.[Jian-Feng], Xu, P.[Peng], Chen, J.Q.[Jian-Qiu], Zhang, K.Y.[Kai-Yuan],
A novel refined maintenance strategy for full life cycle of high-speed automatic train protection system,
IET-ITS(18), No. 5, 2024, pp. 889-903.
DOI Link 2405
intelligent transportation systems, maintenance engineering, rail traffic control, reliability BibRef

Zhou, W.[Wujie], Hong, J.K.[Jian-Kang], Yan, W.Q.[Wei-Qing], Jiang, Q.P.[Qiu-Ping],
Modal Evaluation Network via Knowledge Distillation for No-Service Rail Surface Defect Detection,
CirSysVideo(34), No. 5, May 2024, pp. 3930-3942.
IEEE DOI Code:
WWW Link. 2405
Feature extraction, Task analysis, Encoding, Computational modeling, Predictive models, Adaptation models, surface defect detection BibRef

Zhou, W.[Wujie], Hong, J.K.[Jian-Kang], Ran, X.X.[Xiao-Xiao], Yan, W.Q.[Wei-Qing], Jiang, Q.P.[Qiu-Ping],
DSANet-KD: Dual Semantic Approximation Network via Knowledge Distillation for Rail Surface Defect Detection,
ITS(25), No. 10, October 2024, pp. 13849-13862.
IEEE DOI Code:
WWW Link. 2410
Rails, Defect detection, Decoding, Semantics, Task analysis, Feature extraction, Computational modeling, dual semantic approximation BibRef

Sun, X.Y.[Xin-Yu], Zhou, W.[Wujie], Qian, X.H.[Xiao-Hong],
Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation,
ITS(25), No. 11, November 2024, pp. 16561-16573.
IEEE DOI 2411
Feature extraction, Rails, Defect detection, Computational modeling, Accuracy, Training, Knowledge engineering, rail surface defect detection BibRef

Tang, Q.F.[Qing-Feng], Wei, X.[Xiukun], Wei, D.H.[De-Hua], Shen, X.[Xing], Yin, X.[Xinqiang], Wang, D.[Diqing], Jia, L.M.[Li-Min], Zhong, Q.[Qitian],
High Precision Robust Real-Time Lightweight Approach for Railway Pantograph Slider Wear Estimation,
ITS(25), No. 5, May 2024, pp. 3973-3985.
IEEE DOI 2405
Image processing, Real-time systems, Monitoring, Image edge detection, Deep learning, Head, Rails, Pantograph slider, deep learning BibRef

Su, Z.H.[Zhen-Hua], Luo, J.[Jun], Feng, P.[Piji], Yao, C.X.[Chun-Xing], Yan, Z.Y.[Zhao-Ying], Zhu, T.[Taoning], Zhao, C.[Chunfa], Ma, G.[Guangtong],
Vertical-Lateral Coupled Dynamic Model for Integrated Propulsion, Levitation and Guidance Superconducting EDS Train,
ITS(25), No. 5, May 2024, pp. 4364-4380.
IEEE DOI 2405
Coils, Vehicle dynamics, Mathematical models, Couplings, Electromagnetics, Propulsion, Inductance, levitation and guidance (PLG) coil BibRef

Cheng, R.[Ruijun], Chen, D.[Dewang], Ma, X.P.[Xiao-Ping], Cheng, Y.[Yu], Cheng, H.[Huize],
Intelligent Quantitative Safety Monitoring Approach for ATP Using LSSVM and Probabilistic Model Checking Considering Imperfect Fault Coverage,
ITS(25), No. 5, May 2024, pp. 3724-3738.
IEEE DOI 2405
Safety, Monitoring, Computational modeling, Model checking, Probabilistic logic, Control systems, Rail transportation, online reliability evaluation BibRef

Tong, L.[Lei], Wang, Z.P.[Zhi-Peng], Jia, L.M.[Li-Min], Qin, Y.[Yong], Song, D.H.[Dong-Hai], Miao, B.[Bidong], Tang, T.[Tian], Geng, Y.X.[Yi-Xuan],
TriRNet: Real-Time Rail Recognition Network for UAV-Based Railway Inspection,
ITS(25), No. 5, May 2024, pp. 3927-3943.
IEEE DOI 2405
Rails, Inspection, Rail transportation, Real-time systems, Task analysis, Autonomous aerial vehicles, Mathematical models, automatic railway inspection BibRef

Tan, P.[Ping], Cui, Z.S.[Zhi-Sheng], Wu, Z.G.[Zheng-Guang], Li, X.F.[Xu-Feng], Ding, J.[Jin], Ma, J.[Jien], Huang, B.Q.[Bing-Qiang], Fang, Y.[Youtong],
RTS-LCSS: A New Method for Real-Time Monitoring of Pantograph Structure,
ITS(25), No. 5, May 2024, pp. 3960-3972.
IEEE DOI 2405
Cameras, Real-time systems, Interpolation, Training, Neural networks, Rail transportation, Generators, High-speed railway, pantograph, image processing BibRef

Yang, S.[Songyue], Wang, Z.Y.[Zhang-Yu], Yu, G.Z.[Gui-Zhen], Liu, W.T.[Wen-Tao],
Key Point Estimate Network for Rail-Track Detection,
ITS(25), No. 5, May 2024, pp. 4077-4088.
IEEE DOI 2405
Rails, Transportation, Roads, Rail transportation, Lighting, Accidents, Task analysis, Rail-track detection, pseudo-attention, rail-track generalized IoU BibRef

Nan, Z.L.[Zong-Liang], Zhu, G.[Guoan], Zhang, X.[Xu], Lin, X.[Xuechun], Yang, Y.Y.[Ying-Ying],
Development of a High-Precision Lidar System and Improvement of Key Steps for Railway Obstacle Detection Algorithm,
RS(16), No. 10, 2024, pp. 1761.
DOI Link 2405
BibRef

Li, C.Z.[Chen-Zhong], Sun, H.[Huakun], Li, W.Y.[Wang-Yijia], Wang, Y.F.[Yi-Feng], Wan, Z.[Zhuang], Wu, W.J.[Wei-Jun], Wang, P.[Ping], He, Q.[Qing],
A Multitask Learning Method for Rail Corrugation Detection Using In-Vehicle Responses and Noise Data,
ITS(25), No. 6, June 2024, pp. 5045-5058.
IEEE DOI 2406
Rails, Rail transportation, Inspection, Feature extraction, Vibrations, Maintenance engineering, Data integration, rail track maintenance BibRef

Wang, S.X.[Shi-Xiong], Li, X.K.[Xin-Ke], Chen, Z.[Zhirui], Liu, Y.[Yang],
A Railway Accident Prevention System Using an Intelligent Pilot Vehicle,
ITS(25), No. 6, June 2024, pp. 5170-5188.
IEEE DOI 2406
Rail transportation, Railway accidents, Monitoring, Wireless sensor networks, Maintenance engineering, deep neural network BibRef

Pu, H.[Hao], Wang, G.H.[Guang-Hui], Song, T.[Taoran], Schonfeld, P.[Paul], Zhang, H.[Hong],
Multi-Task Deep Learning Methods for Determining Railway Major Technical Standards,
ITS(25), No. 6, June 2024, pp. 5904-5918.
IEEE DOI 2406
Rail transportation, Decision making, Task analysis, Multitasking, Voting, Feature extraction, Environmental factors, Decision making, railway technical standards BibRef

He, Y.Y.X.[Yuan-Yan-Xingzi], Li, Y.W.[Yong-Wei], Xu, L.R.[Lin-Rong],
An Integrated Multisource and Multiscale Monitoring Technique for Assessing the Health Status of High-Speed Railway Subgrade,
RS(16), No. 11, 2024, pp. 1972.
DOI Link 2406
BibRef

Wang, J.[Jie], Li, G.Q.[Guo-Qiang], Qiu, G.[Guanwen], Ma, G.[Gang], Xi, J.W.[Jin-Wen], Yu, N.[Nana],
Depth-Assisted Semi-Supervised RGB-D Rail Surface Defect Inspection,
ITS(25), No. 7, July 2024, pp. 8042-8052.
IEEE DOI 2407
Task analysis, Rails, Annotations, Inspection, Training, Surface morphology, Shape, Rail surface defect inspection, salient object detection BibRef

Cao, Z.W.[Zhi-Wei], Qin, Y.[Yong], Jia, L.M.[Li-Min], Xie, Z.Y.[Zheng-Yu], Gao, Y.[Yang], Wang, Y.[Yaguan], Li, P.[Ping], Yu, Z.[Zujun],
Railway Intrusion Detection Based on Machine Vision: A Survey, Challenges, and Perspectives,
ITS(25), No. 7, July 2024, pp. 6427-6448.
IEEE DOI 2407
Rail transportation, Intrusion detection, Monitoring, Machine vision, Inspection, Autonomous aerial vehicles, deep learning BibRef

Wu, Y.K.[Yun-Kai], Su, Y.[Yu], Wang, Y.L.[Yu-Long], Shi, P.[Peng],
T-S Fuzzy Data-Driven ToMFIR With Application to Incipient Fault Detection and Isolation for High-Speed Rail Vehicle Suspension Systems,
ITS(25), No. 7, July 2024, pp. 7921-7932.
IEEE DOI 2407
Rail transportation, Fault detection, Sensors, Monitoring, Data models, Analytical models, Actuators, Data-driven ToMFIR, suspension systems BibRef

He, S.[Sen], Jian, Z.[Zehua], Liu, S.L.[Shao-Li], Liu, J.H.[Jian-Hua], Fang, Y.[Yue], Hu, J.[Jia],
PCSGAN: A Perceptual Constrained Generative Model for Railway Defect Sample Expansion From a Single Image,
ITS(25), No. 8, August 2024, pp. 8796-8806.
IEEE DOI 2408
Rail transportation, Defect detection, Training, Generative adversarial networks, Deep learning, Rails, Fasteners, railway defect detection BibRef

Chen, X.H.[Xiao-Hong], Jin, Z.[Zhi], Zhang, M.[Min], Mallet, F.[Frédéric], Liu, X.S.[Xiao-Shan], Zhou, T.L.[Ting-Liang],
A Scalable Approach to Detecting Safety Requirements Inconsistencies for Railway Systems,
ITS(25), No. 8, August 2024, pp. 8375-8386.
IEEE DOI 2408
Clocks, Safety, Rail transportation, Unified modeling language, Cause effect analysis, Scalability, railway systems BibRef

Cao, Z.W.[Zhi-Wei], Gao, Y.[Yang], Bai, J.[Jie], Qin, Y.[Yong], Zheng, Y.[Yuanjin], Jia, L.M.[Li-Min],
Efficient Dual-Stream Fusion Network for Real-Time Railway Scene Understanding,
ITS(25), No. 8, August 2024, pp. 9442-9452.
IEEE DOI 2408
Rail transportation, Feature extraction, Semantic segmentation, Semantics, Safety, Rails, Real-time systems, autonomous driving BibRef

Wang, H.[Huan], Li, Y.F.[Yan-Fu], Men, T.[Tianli],
Wavelet Integrated CNN With Dynamic Frequency Aggregation for High-Speed Train Wheel Wear Prediction,
ITS(25), No. 8, August 2024, pp. 8960-8971.
IEEE DOI 2408
Wheels, Discrete wavelet transforms, Convolutional neural networks, Feature extraction BibRef

Li, J.W.[Jia-Wei], Tian, D.X.[Da-Xin], Zhou, J.[Jianshan], Duan, X.[Xuting], Sheng, Z.G.[Zheng-Guo], Zhao, D.[Dezong], Cao, D.[Dongpu],
Distributed Robust Model Predictive Control for Virtual Coupling Under Structural and External Uncertainty,
ITS(25), No. 8, August 2024, pp. 8751-8769.
IEEE DOI 2408
Stability analysis, Couplings, Uncertainty, Rail transportation, Control systems, Feedforward systems, Formation control, uncertainty BibRef

Chang, Y.Q.[Yong-Qi], Zhang, X.[Xin], Song, S.Z.[Shu-Zhi], Song, Q.H.[Qing-Hua], Zhao, Z.Y.[Zhen-Yu], Wang, W.[Wensong], Jie, H.M.[Hua-Min], Shen, Y.[Yi],
EMAE-Based Rail Structural Health Monitoring Using Double-Layer Signal Processing and Spectrum Information Entropy,
ITS(25), No. 9, September 2024, pp. 11370-11381.
IEEE DOI 2409
Rails, Monitoring, Signal processing algorithms, Signal processing, Electromagnetics, Rail transportation, Interference, spectrum information entropy (SIE) BibRef

Feng, Y.[Yong], Chen, J.L.[Jing-Long], Xie, Z.L.[Zong-Liang], Xie, J.S.[Jing-Song], Pan, T.[Tongyang], Li, C.[Chao], Zhao, Q.[Qing],
Integrating Misidentification and OOD Detection for Reliable Fault Diagnosis of High-Speed Train Bogie,
ITS(25), No. 9, September 2024, pp. 11935-11945.
IEEE DOI 2409
Uncertainty, Reliability, Fault diagnosis, Bayes methods, Probabilistic logic, Predictive models, Data models, uncertainty estimation BibRef

Shang, D.[Du], Su, S.[Shuai], Sun, Y.[Yongkui], Wang, F.[Feng], Cao, Y.[Yuan], Liu, H.T.[Hai-Tao], Yang, W.F.[Wei-Feng], Zhou, J.H.[Ji-Hui],
Fault Diagnosis for Rail Profile Data Using Refined Dispersion Entropy and Dependence Measurements,
ITS(25), No. 9, September 2024, pp. 11689-11700.
IEEE DOI 2409
Rails, Entropy, Correlation, Dispersion, Rail transportation, Fault diagnosis, Complexity theory, Fault diagnosis, rail profile, martingale difference correlation BibRef

Ni, X.F.[Xue-Feng], Fieguth, P.W.[Paul W.], Ma, Z.[Ziji], Shi, B.[Bo], Qiu, Y.[Yuan], Chen, Y.H.[Yu-Hao], Liu, H.L.[Hong-Li],
Superpixel-Guided Multi-Type Rail Segmentation via Contextual Information Aggregation,
ITS(25), No. 10, October 2024, pp. 14004-14018.
IEEE DOI 2410
Rails, Rail transportation, Semantics, Image segmentation, Noise, Inspection, Feature extraction, Semantic segmentation, rail segmentation BibRef

Tong, S.Z.[Shu-Zhen], Wang, Q.[Qing], Wei, X.[Xuan], Lu, C.[Cheng], Lu, X.B.[Xiao-Bo],
Modulated deformable convolution based on graph convolution network for rail surface crack detection,
SP:IC(130), 2025, pp. 117202.
Elsevier DOI 2412
Rail Surface, Crack Detection, Deformable convolution, Semantic segmentation BibRef

Wang, Y.[Yi], Cai, X.P.[Xiao-Pei], Tang, X.[Xueyang], Pan, S.[Shuo], Wang, Y.Q.[Yu-Qi], Yan, H.[Hai], Ren, Y.H.[Yu-Heng], Hou, Y.[Yue],
HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway,
ITS(25), No. 12, December 2024, pp. 20793-20803.
IEEE DOI 2412
Monitoring, Anomaly detection, Data models, Noise, Generative adversarial networks, Statistical analysis BibRef

Zhao, Z.[Zheda], Qin, Y.[Yong], Qian, Y.[Yu], Wu, Y.P.[Yun-Peng], Qin, W.W.[Wen-Wen], Zhang, H.[Hao], Wu, X.L.[Xiao-Lei],
Automatic Potential Safety Hazard Evaluation System for Environment Around High-Speed Railroad Using Hybrid U-Shape Learning Architecture,
ITS(26), No. 1, January 2025, pp. 1071-1087.
IEEE DOI 2501
Autonomous aerial vehicles, Hazards, YOLO, Inspection, Feature extraction, Semantics, Roads, Rail transportation, Training, hazard evaluation BibRef

Liu, Y.Y.[Yu-Yao], Li, Q.Y.[Qing-Yong], Bao, S.[Shi], Wang, W.[Wen],
Multi-Scale Rail Surface Anomaly Detection Based on Weighted Multivariate Gaussian Distribution,
IEICE(E108-D), No. 2, February 2025, pp. 147-156.
WWW Link. 2502
BibRef

Zhang, L.X.[Long-Xin], Zeng, W.L.[Wen-Liang], Zhou, P.[Peng], Deng, X.J.[Xiao-Jun], Wu, J.[Jiayu], Wen, H.[Hong],
A fast and lightweight train image fault detection model based on convolutional neural networks,
IVC(154), 2025, pp. 105380.
Elsevier DOI 2502
Attention mechanism, Clustering algorithm, Convolutional neural network, Fault detection BibRef

Liu, W.[Wei], Lu, X.B.[Xiao-Bo], Wei, Y.[Yun], Ran, Z.D.[Zhi-Dan],
MFECNet: Multi-level feature enhancement and correspondence network for few-shot anomaly detection of high-speed trains,
PR(161), 2025, pp. 111298.
Elsevier DOI 2502
Anomaly detection, Few-shot learning, MFECNet BibRef

Wu, Y.[Yue], Qiang, F.F.[Fang-Fang], Zhou, W.[Wujie], Yan, W.Q.[Wei-Qing],
PFCNet: Enhancing Rail Surface Defect Detection With Pixel-Aware Frequency Conversion Networks,
SPLetters(32), 2025, pp. 606-610.
IEEE DOI 2502
Convolution, Feature extraction, Rails, Object detection, Frequency conversion, Surface treatment, Semantics, Inspection, DCT transform BibRef

Santamato, G.[Giancarlo], Tozzetti, L.[Lorenzo], Solazzi, M.[Massimiliano], Fedeli, E.[Eugenio], di Pasquale, F.[Fabrizio],
SmartRail: A System for the Continuous Monitoring of the Track Geometry Based on Embedded Arrays of Fiber Optic Sensors,
ITS(26), No. 3, March 2025, pp. 3262-3272.
IEEE DOI 2503
Strain, Sensors, Rails, Optical fiber sensors, Sensor arrays, Geometry, Temperature measurement, Temperature sensors, Fiber gratings, finite elements analysis BibRef

Cao, Y.[Yefan], Pan, H.X.[Hai-Xia], Wang, H.Q.[Hong-Qiang], Xu, X.[Xin], Li, Y.[Yanan], Tian, Z.H.[Zhao-Hui], Zhao, X.R.[Xiao-Ran],
Small Object Detection Algorithm for Railway Scene,
ICIVC22(100-105)
IEEE DOI 2301
Data preprocessing, Object detection, Feature extraction, Rail transportation, railway scene, small objects, YOLOv5, data augmentation BibRef

Chen, Z.X.[Zheng-Xing], Wang, Q.H.[Qi-Hang], He, Q.[Qing], Yu, T.[Tianle], Zhang, M.[Min], Wang, P.[Ping],
CUFuse: Camera and Ultrasound Data Fusion for Rail Defect Detection,
ITS(23), No. 11, November 2022, pp. 21971-21983.
IEEE DOI 2212
Rails, Feature extraction, Surface treatment, Image edge detection, Inspection, Acoustics, Rail transportation, Rail defect, data fusion, convolutional neural networks BibRef