19.7.3.9.2 Inspection -- Pavement, Road Surface, Asphalt, Concrete

Chapter Contents (Back)
Pavement Analysis. Concrete Inspection. Crack Detection. Application, Inspection. Inspection, Defects. Defect Detection. Bridges, deformation, structural:
See also Deformation of Bridges, Monitor Bridges, Other Structures. Other things on the road
See also Obstacle Dectection, Objects on the Road.

Kalliomäki, I.[Ilkka], Vehtari, A.[Aki], Lampinen, J.[Jouko],
Shape analysis of concrete aggregates for statistical quality modeling,
MVA(16), No. 3, May 2005, pp. 197-201.
Springer DOI 0505
BibRef

Le Bastard, C., Baltazart, V., Wang, Y., Saillard, J.,
Thin-Pavement Thickness Estimation Using GPR With High-Resolution and Superresolution Methods,
GeoRS(45), No. 8, August 2007, pp. 2511-2519.
IEEE DOI 0709
BibRef

Bourlier, C., Le Bastard, C., Baltazart, V.,
Generalization of PILE Method to the EM Scattering From Stratified Subsurface With Rough Interlayers: Application to the Detection of Debondings Within Pavement Structure,
GeoRS(53), No. 7, July 2015, pp. 4104-4115.
IEEE DOI 1503
Ground penetrating radar BibRef

Yamaguchi, T.[Tomoyuki], Hashimoto, S.[Shuji],
Fast crack detection method for large-size concrete surface images using percolation-based image processing,
MVA(21), No. 5, August 2010, pp. 797-809.
WWW Link. 1011
BibRef
Earlier:
Improved percolation-based method for crack detection in concrete surface images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Clemmensen, L.H.[Line H.], Hansen, M.E.[Michael E.], Ersbřll, B.K.[Bjarne K.],
A comparison of dimension reduction methods with application to multi-spectral images of sand used in concrete,
MVA(21), No. 6, October 2010, pp. 959-968.
WWW Link. 1011
BibRef

Fujita, Y.[Yusuke], Hamamoto, Y.[Yoshihiko],
A robust automatic crack detection method from noisy concrete surfaces,
MVA(22), No. 2, March 2011, pp. 245-254.
WWW Link. 1103
BibRef

Elunai, R., Chandran, V., Gallagher, E.,
Asphalt Concrete Surfaces Macrotexture Determination From Still Images,
ITS(12), No. 3, September 2011, pp. 857-869.
IEEE DOI 1109
BibRef

Li, Q.Q.[Qing-Quan], Zou, Q.[Qin], Zhang, D.Q.[Da-Qiang], Mao, Q.Z.[Qing-Zhou],
FoSA: F* Seed-growing Approach for crack-line detection from pavement images,
IVC(29), No. 12, November 2011, pp. 861-872.
Elsevier DOI 1112
Line detection; Pavement crack; Seed-growing; Dynamic programming BibRef

Zou, Q.[Qin], Cao, Y.[Yu], Li, Q.Q.[Qing-Quan], Mao, Q.Z.[Qing-Zhou], Wang, S.[Song],
CrackTree: Automatic crack detection from pavement images,
PRL(33), No. 3, 1 February 2012, pp. 227-238.
Elsevier DOI 1201
Crack detection; Edge detection; Edge grouping; Tensor voting; Shadow removal BibRef

Suanpaga, W., Yoshikazu, K.,
Riding Quality Model for Asphalt Pavement Monitoring Using Phase Array Type L-band Synthetic Aperture Radar (PALSAR),
RS(2), No. 11, November 2010, pp. 2531-2546.
DOI Link 1203
BibRef

Ndoye, M., Barker, A.M., Krogmeier, J.V., Bullock, D.M.,
A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road-Condition-Monitoring System,
ITS(12), No. 3, September 2011, pp. 795-808.
IEEE DOI 1109
BibRef

Oliveira, H., Correia, P.L.,
Automatic Road Crack Detection and Characterization,
ITS(14), No. 1, March 2013, pp. 155-168.
IEEE DOI 1303
BibRef

Fang, H., Lin, G., Zhang, R.,
The First-Order Symplectic Euler Method for Simulation of GPR Wave Propagation in Pavement Structure,
GeoRS(51), No. 1, January 2013, pp. 93-98.
IEEE DOI 1301
BibRef

Murthy, S.B.S., Varaprasad, G.,
Detection of potholes in autonomous vehicle,
IET-ITS(8), No. 6, September 2014, pp. 543-549.
DOI Link 1411
collision avoidance BibRef

Shangguan, P.C.[Peng-Cheng], Al-Qadi, I.L.,
Calibration of FDTD Simulation of GPR Signal for Asphalt Pavement Compaction Monitoring,
GeoRS(53), No. 3, March 2015, pp. 1538-1548.
IEEE DOI 1412
asphalt BibRef

Guan, H.[Haiyan], Li, J., Yu, Y.T.[Yong-Tao], Chapman, M., Wang, H.Y.[Han-Yun], Wang, C.[Cheng], Zhai, R.F.[Rui-Fang],
Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data,
GeoRS(53), No. 3, March 2015, pp. 1527-1537.
IEEE DOI 1412
crack detection
See also Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data. BibRef

Yi, C., Chuang, Y., Nian, C.,
Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies,
ITS(16), No. 4, August 2015, pp. 1905-1917.
IEEE DOI 1508
Feature extraction BibRef

Rajamohan, D.[Deepak], Gannu, B.[Bhavana], Rajan, K.S.[Krishnan Sundara],
MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data,
IJGI(4), No. 3, 2015, pp. 1225.
DOI Link 1508
BibRef

Mathavan, S., Kamal, K., Rahman, M.,
A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements,
ITS(16), No. 5, October 2015, pp. 2353-2362.
IEEE DOI 1511
Survey, Pavement Analysis. computer vision BibRef

Quintana, M., Torres, J., Menéndez, J.M.,
A Simplified Computer Vision System for Road Surface Inspection and Maintenance,
ITS(17), No. 3, March 2016, pp. 608-619.
IEEE DOI 1603
Cameras BibRef

Zhang, S.[Su], Lippitt, C.D.[Christopher D.], Bogus, S.M.[Susan M.], Neville, P.R.H.[Paul R. H.],
Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography,
RS(8), No. 5, 2016, pp. 392.
DOI Link 1606
BibRef

Hoult, N.A., Dutton, M., Hoag, A., Take, W.A.,
Measuring Crack Movement in Reinforced Concrete Using Digital Image Correlation: Overview and Application to Shear Slip Measurements,
PIEEE(104), No. 8, August 2016, pp. 1561-1574.
IEEE DOI 1608
Area measurement BibRef

Amhaz, R.[Rabih], Chambon, S.[Sylvie], Idier, J.[Jerome], Baltazart, V.[Vincent],
Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection,
ITS(17), No. 10, October 2016, pp. 2718-2729.
IEEE DOI 1610
BibRef
Earlier:
A new minimal path selection algorithm for automatic crack detection on pavement images,
ICIP14(788-792)
IEEE DOI 1502
Context Cost function BibRef

Ishikawa, T.[Tsuyoshi], Fujinami, K.[Kaori],
Smartphone-Based Pedestrian's Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection,
IJGI(5), No. 10, 2016, pp. 182.
DOI Link 1610
BibRef

Jang, D.W., Park, R.H.,
Pothole detection using spatio-temporal saliency,
IET-ITS(10), No. 9, 2016, pp. 605-612.
DOI Link 1609
asphalt BibRef

Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.,
Automatic Road Crack Detection Using Random Structured Forests,
ITS(17), No. 12, December 2016, pp. 3434-3445.
IEEE DOI 1612
Feature extraction BibRef

Zaini, N.[Nasrullah], van der Meer, F.[Freek], van Ruitenbeek, F.[Frank], de Smeth, B.[Boudewijn], Amri, F.[Fadli], Lievens, C.[Caroline],
An Alternative Quality Control Technique for Mineral Chemistry Analysis of Portland Cement-Grade Limestone Using Shortwave Infrared Spectroscopy,
RS(8), No. 11, 2016, pp. 950.
DOI Link 1612
BibRef

Zhang, D.[Dejin], Li, Q.Q.[Qing-Quan], Chen, Y.[Ying], Cao, M.[Min], He, L.[Li], Zhang, B.L.[Bai-Ling],
An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection,
IVC(57), No. 1, 2017, pp. 130-146.
Elsevier DOI 1702
Pavement crack detection BibRef

Casselgren, J.[Johan], Bodin, U.[Ulf],
Reusable road condition information system for traffic safety and targeted maintenance,
IET-ITS(11), No. 4, May 2017, pp. 230-238.
DOI Link 1705
BibRef

Lenglet, C.[Céline], Blanc, J.[Juliette], Dubroca, S.[Stéphane],
Smart road that warns its network manager when it begins cracking,
IET-ITS(11), No. 3, April 2017, pp. 152-157.
DOI Link 1705
BibRef

González, L.C., Moreno, R., Escalante, H.J., Martínez, F., Carlos, M.R.,
Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation,
ITS(18), No. 11, November 2017, pp. 2916-2928.
IEEE DOI 1711
Accelerometers, Automobiles, Data collection, Roads, Sensors, Smart phones, Urban areas, Roadway surface disruptions, accelerometer. BibRef

Carmon, N.[Nimrod], Ben-Dor, E.[Eyal],
Mapping Asphaltic Roads' Skid Resistance Using Imaging Spectroscopy,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Carlos, M.R., Aragón, M.E., González, L.C., Escalante, H.J., Martínez, F.,
Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings: Addressing Who's Who,
ITS(19), No. 10, October 2018, pp. 3334-3343.
IEEE DOI 1810
Roads, Accelerometers, Sensors, Support vector machines, Proposals, Acceleration, Accelerometer measurements, mobile sensing, road anomalies BibRef

Zhang, Y., Chen, C., Wu, Q., Lu, Q., Zhang, S., Zhang, G., Yang, Y.,
A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition,
ITS(19), No. 12, December 2018, pp. 3935-3946.
IEEE DOI 1812
Surface cracks, Surface reconstruction, Sensors, Image reconstruction, BibRef

Zhao, S., Al-Qadi, I.L.,
Super-Resolution of 3-D GPR Signals to Estimate Thin Asphalt Overlay Thickness Using the XCMP Method,
GeoRS(57), No. 2, February 2019, pp. 893-901.
IEEE DOI 1901
Signal resolution, Ground penetrating radar, Dielectric constant, Antennas, Image resolution, Asphalt, Estimation, thin asphalt overlay BibRef

Li, Z.Q.[Zhi-Qiang], Cheng, C.Q.[Cheng-Qi], Kwan, M.P.[Mei-Po], Tong, X.C.[Xiao-Chong], Tian, S.H.[Shao-Hong],
RETRACTION: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
BibRef
And: IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Feng, H.[Hui], Xu, G.S.[Guo-Sheng], Guo, Y.H.[Yan-Hui],
Multi-scale classification network for road crack detection,
IET-ITS(13), No. 2, February 2019, pp. 398-405.
DOI Link 1902
BibRef

Li, H., Song, D., Liu, Y., Li, B.,
Automatic Pavement Crack Detection by Multi-Scale Image Fusion,
ITS(20), No. 6, June 2019, pp. 2025-2036.
IEEE DOI 1906
Training data, Manuals, Feature extraction, Training, Clutter, Image edge detection, Fuses, Crack detection, robotic airport runway inspection BibRef

Kaddah, W.[Wissam], Elbouz, M.[Marwa], Ouerhani, Y.[Yousri], Baltazart, V.[Vincent], Alfalou, A.[Ayman],
Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images,
VC(35), No. 9, September 2018, pp. 1293-1309.
Springer DOI 1908
BibRef

Tan, Y.M.[Yu-Min], Li, Y.X.[Yun-Xin],
UAV Photogrammetry-Based 3D Road Distress Detection,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Fan, R., Ozgunalp, U., Hosking, B., Liu, M., Pitas, I.,
Pothole Detection Based on Disparity Transformation and Road Surface Modeling,
IP(29), No. 1, 2020, pp. 897-908.
IEEE DOI 1910
BibRef
And: Corrections: IP(29), 2020, pp. 3091-3091.
IEEE DOI 2002
Pothole detection, road surface modeling. Roads, Surface treatment, Surface reconstruction, Detection algorithms, Sea surface, surface normal BibRef

Yang, W.W.[Wen-Wei],
Finite element model of concrete material based on CT image processing technology,
JVCIR(64), 2019, pp. 102631.
Elsevier DOI 1911
CT image, Numerical model, Concrete, Failure process BibRef

Cheng, L.[Lushan], Zhang, X.[Xu], Shen, J.[Jie],
Road surface condition classification using deep learning,
JVCIR(64), 2019, pp. 102638.
Elsevier DOI 1911
Deep learning, Road condition, Activation function, Image recognition, Intelligent driving BibRef

Hadavandsiri, Z.[Zahra], Lichti, D.D.[Derek D.], Jahraus, A.[Adam], Jarron, D.[David],
Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Mohammadi, M.E.[Mohammad Ebrahim], Wood, R.L.[Richard L.], Wittich, C.E.[Christine E.],
Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Fei, Y., Wang, K.C.P., Zhang, A., Chen, C., Li, J.Q., Liu, Y., Yang, G., Li, B.,
Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V,
ITS(21), No. 1, January 2020, pp. 273-284.
IEEE DOI 2001
Surface cracks, Asphalt, Libraries, Feature extraction, Deep learning, Kernel, CrackNet, CrackNet-V, surface cracks BibRef

de Blasiis, M.R.[Maria Rosaria], di Benedetto, A.[Alessandro], Fiani, M.[Margherita],
Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Kashiyama, T.[Takehiro], Sekimoto, Y.[Yoshihide], Seto, T.[Toshikazu], Lwin, K.K.[Ko Ko],
Analyzing Road Coverage of Public Vehicles According to Number and Time Period for Installation of Road Inspection Systems,
IJGI(9), No. 3, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., Ling, H.,
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection,
ITS(21), No. 4, April 2020, pp. 1525-1535.
IEEE DOI 2004
Feature extraction, Image edge detection, Deep learning, Boosting, Task analysis, Semantics, Wavelet transforms, hierarchical boosting BibRef

Pu, Z.[Ziyuan], Cui, Z.Y.[Zhi-Yong], Wang, S.[Shuo], Li, Q.[Qianmu], Wang, Y.[Yinhai],
Time-aware gated recurrent unit networks for forecasting road surface friction using historical data with missing values,
IET-ITS(14), No. 4, April 2020, pp. 213-219.
DOI Link 2004
BibRef

Kaddah, W.[Wissam], Elbouz, M.[Marwa], Ouerhani, Y.[Yousri], Alfalou, A.[Ayman], Desthieux, M.[Marc],
Automatic darkest filament detection (ADFD): a new algorithm for crack extraction on two-dimensional pavement images,
VC(36), No. 7, July 2020, pp. 1369-1384.
Springer DOI 2005
BibRef

Meyer, F.J.[Franz J.], Ajadi, O.A.[Olaniyi A.], Hoppe, E.J.[Edward J.],
Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Xiang, X.Z.[Xue-Zhi], Zhang, Y.Q.[Yu-Qi], El Saddik, A.[Abdulmotaleb],
Pavement crack detection network based on pyramid structure and attention mechanism,
IET-IPR(14), No. 8, 19 June 2020, pp. 1580-1586.
DOI Link 2005
BibRef

Zou, L., Yi, L., Sato, M.,
On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System,
GeoRS(58), No. 6, June 2020, pp. 4215-4224.
IEEE DOI 2005
Asphalt airport pavement, common midpoint (CMP), ground-penetrating radar (GPR), interlayer debonding detection, nondestructive inspection BibRef

Du, Y., Liu, C., Song, Y., Li, Y., Shen, Y.,
Rapid Estimation of Road Friction for Anti-Skid Autonomous Driving,
ITS(21), No. 6, June 2020, pp. 2461-2470.
IEEE DOI 2006
Roads, Friction, Resistance, Electrical resistance measurement, Standards, Autonomous vehicles, Immune system, Autonomous vehicle, velocity control BibRef

Rodés, J.P.[Josep Pedret], Reguero, A.M.[Adriana Martínez], Pérez-Gracia, V.[Vega],
GPR Spectra for Monitoring Asphalt Pavements,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Debroux, N.[Noémie], Le Guyader, C.[Carole], Vese, L.A.[Luminita A.],
A Nonlocal Laplacian-Based Model for Bituminous Surfacing Crack Recovery and its MPI Implementation,
JMIV(62), No. 6-7, July 2020, pp. 1007-1033.
Springer DOI 2007
BibRef

Dhiman, A., Klette, R.,
Pothole Detection Using Computer Vision and Learning,
ITS(21), No. 8, August 2020, pp. 3536-3550.
IEEE DOI 2008
Roads, Image reconstruction, Shape, Accelerometers, Cameras, deep learning BibRef

Abdellatif, M.[Mohamed], Peel, H.[Harriet], Cohn, A.G.[Anthony G.], Fuentes, R.[Raul],
Pavement Crack Detection from Hyperspectral Images Using A Novel Asphalt Crack Index,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Fan, R., Liu, M.,
Road Damage Detection Based on Unsupervised Disparity Map Segmentation,
ITS(21), No. 11, November 2020, pp. 4906-4911.
IEEE DOI 2011
Roads, Image segmentation, Cameras, Sensors, numerical solution BibRef

Fan, R.[Rui], Ozgunalp, U.[Umar], Wang, Y.[Yuan], Liu, M.[Ming], Pitas, I.[Ioannis],
Rethinking Road Surface 3-D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation,
Cyber(52), No. 7, July 2022, pp. 5799-5808.
IEEE DOI 2207
Roads, Surface morphology, Sensors, Cameras, Surface reconstruction, Estimation, Disparity map transformation, simple linear iterative clustering BibRef

Mattheuwsen, L.[Lukas], Vergauwen, M.[Maarten],
Manhole Cover Detection on Rasterized Mobile Mapping Point Cloud Data Using Transfer Learned Fully Convolutional Neural Networks,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Özdemir, O.B.[Okan Bilge], Soydan, H.[Hilal], Çetin, Y. .Y.[Yasemin Yardimci], Düzgün, H.S.[Hafize Sebnem],
Neural Network Based Pavement Condition Assessment with Hyperspectral Images,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Mettas, C.[Christodoulos], Evagorou, E.[Evagoras], Agapiou, A.[Athos], Hadjimitsis, D.[Diofantos],
The Use of Colorimeters to Support Remote Sensing Techniques on Asphalt Pavements,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Fiorentini, N.[Nicholas], Maboudi, M.[Mehdi], Leandri, P.[Pietro], Losa, M.[Massimo], Gerke, M.[Markus],
Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Yu, Y.[Yang], Rashidi, M.[Maria], Samali, B.[Bijan], Yousefi, A.M.[Amir M.], Wang, W.Q.[Wei-Qiang],
Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D-S Fusion Algorithm for Bridge Structures,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zhang, K., Zhang, Y., Cheng, H.D.,
CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning,
ITS(22), No. 2, February 2021, pp. 1306-1319.
IEEE DOI 2102
Training, Feature extraction, Generative adversarial networks, Image segmentation, Generators, Semantics, partially accurate ground truths BibRef

Liu, Z.[Zhen], Wu, W.X.[Wen-Xiu], Gu, X.Y.[Xing-Yu], Li, S.W.[Shu-Wei], Wang, L.[Lutai], Zhang, T.J.[Tian-Jie],
Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Pan, J., Sun, M., Wang, Y., Le Bastard, C., Baltazart, V.,
Time-Delay Estimation by a Modified Orthogonal Matching Pursuit Method for Rough Pavement,
GeoRS(59), No. 4, April 2021, pp. 2973-2981.
IEEE DOI 2104
Ground penetrating radar, Matching pursuit algorithms, Estimation, Frequency measurement, Media, Data models, time-delay estimation (TDE) BibRef

Dérobert, X.[Xavier], Baltazart, V.[Vincent], Simonin, J.M.[Jean-Michel], Todkar, S.S.[Shreedhar Savant], Norgeot, C.[Christophe], Hui, H.Y.[Ho-Yan],
GPR Monitoring of Artificial Debonded Pavement Structures throughout Its Life Cycle during Accelerated Pavement Testing,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Shen, R.Q.[Rui-Qing], Zhao, Y.H.[Yong-Hui], Hu, S.F.[Shu-Fan], Li, B.[Bo], Bi, W.[Wenda],
Reverse-Time Migration Imaging of Ground-Penetrating Radar in NDT of Reinforced Concrete Structures,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Xu, J.C.[Jun-Cai], Zhang, J.K.[Jing-Kui], Sun, W.G.[Wei-Gang],
Recognition of the Typical Distress in Concrete Pavement Based on GPR and 1D-CNN,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Diamanti, N.[Nectaria], Annan, A.P.[A. Peter], Jackson, S.R.[Steven R.], Klazinga, D.[Dylan],
A GPR-Based Pavement Density Profiler: Operating Principles and Applications,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Bhattacharya, G.[Gaurab], Mandal, B.[Bappaditya], Puhan, N.B.[Niladri B.],
Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification,
IP(30), 2021, pp. 6957-6969.
IEEE DOI 2108
Feature extraction, Computer architecture, Concrete, Aggregates, Inspection, Monitoring, Meteorology, Fine-grained dense module, multi-target multi-class classification BibRef

Bhattacharya, G.[Gaurab], Mandal, B.[Bappaditya], Puhan, N.B.[Niladri B.],
Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification,
CirSysVideo(31), No. 9, September 2021, pp. 3707-3713.
IEEE DOI 2109
Feature extraction, Corrosion, Bars, Tensile stress, Data mining, Distance measurement, Computer architecture, multi-target multi-class classification BibRef

Tankeu, B.T.[Bachir Tchana], Baltazart, V.[Vincent], Wang, Y.[Yide], Guilbert, D.[David],
PUMA Applied to Time Delay Estimation for Processing GPR Data over Debonded Pavement Structures,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Rasol, M.[Mezgeen], Schmidt, F.[Franziska], Ientile, S.[Silvia], Adelaide, L.[Lucas], Nedjar, B.[Boumediene], Kane, M.[Malal], Chevalier, C.[Christophe],
Progress and Monitoring Opportunities of Skid Resistance in Road Transport: A Critical Review and Road Sensors,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Song, Y.Z.[Yong-Ze], Wu, P.[Peng], Gilmore, D.[Daniel], Li, Q.[Qindong],
A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems,
ITS(22), No. 11, November 2021, pp. 7073-7083.
IEEE DOI 2112
Roads, Monitoring, Australia, Data models, Data analysis, Spatial databases, Image segmentation, spatial analysis BibRef

Fan, R.[Rui], Wang, H.[Hengli], Wang, Y.[Yuan], Liu, M.[Ming], Pitas, I.[Ioannis],
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms,
IP(30), 2021, pp. 8144-8154.
IEEE DOI 2110
Roads, Image segmentation, Semantics, Convolutional neural networks, Feature extraction, graph neural network BibRef

Cao, Q.Q.[Qing-Qing], Al-Qadi, I.L.[Imad L.],
Effect of Moisture Content on Calculated Dielectric Properties of Asphalt Concrete Pavements from Ground-Penetrating Radar Measurements,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Chen, C.[Cheng], Chandra, S.[Sindhu], Han, Y.F.[Yu-Fan], Seo, H.[Hyungjoon],
Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Wei, Z.X.[Zi-Xian], SUN, T.[Tao], Wu, Y.[Yuhao], Zhou, L.Q.[Li-Qing], Ruan, X.L.[Xiao-Li],
Pavement crack detection using non-local theory and iterative sampling,
IET-IPR(16), No. 3, 2022, pp. 869-877.
DOI Link 2202
BibRef

Fan, L.[Lili], Zhao, H.W.[Hong-Wei], Li, Y.[Ying], Li, S.[Shen], Zhou, R.[Rui], Chu, W.[Wenbo],
RAO-UNet: a residual attention and octave UNet for road crack detection via balance loss,
IET-ITS(16), No. 3, 2022, pp. 332-343.
DOI Link 2202
BibRef

Sun, M.[Mingsi], Zhao, H.W.[Hong-Wei], Li, J.[Jiao],
Road crack detection network under noise based on feature pyramid structure with feature enhancement (road crack detection under noise),
IET-IPR(16), No. 3, 2022, pp. 809-822.
DOI Link 2202
BibRef

Simonin, J.M.[Jean-Michel], Piau, J.M.[Jean-Michel], Le-Boursicault, V.[Vinciane], Freitas, M.[Murilo],
Orthogonal Set of Indicators for the Assessment of Flexible Pavement Stiffness from Deflection Monitoring: Theoretical Formalism and Numerical Study,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Guo, S.[Shili], Xu, Z.W.[Zhi-Wei], Li, X.[Xiuzhong], Zhu, P.[Peimin],
Detection and Characterization of Cracks in Highway Pavement with the Amplitude Variation of GPR Diffracted Waves: Insights from Forward Modeling and Field Data,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhang, L.L.[Liang-Liang], Wang, L.[Lin], Yang, B.[Bo], Niu, S.[Sijie], Han, Y.M.[Ya-Min], Oh, S.K.[Sung-Kwun],
Rapid construction of 4D high-quality microstructural image for cement hydration using partial information registration,
PR(124), 2022, pp. 108471.
Elsevier DOI 2203
Cement hydration, Rapid image construction, Image registration, Particle swarm optimization, Microstructural temporal image sequences BibRef

Zhou, Q.[Qiang], Qu, Z.[Zhong], Ju, F.R.[Fang-Rong],
A multi-scale learning method with dilated convolutional network for concrete surface cracks detection,
IET-IPR(16), No. 5, 2022, pp. 1389-1402.
DOI Link 2203
BibRef

Yu, Y.T.[Yong-Tao], Guan, H.Y.[Hai-Yan], Li, D.[Dilong], Zhang, Y.J.[Yong-Jun], Jin, S.H.[Sheng-Hua], Yu, C.H.[Chang-Hui],
CCapFPN: A Context-Augmented Capsule Feature Pyramid Network for Pavement Crack Detection,
ITS(23), No. 4, April 2022, pp. 3324-3335.
IEEE DOI 2204
Feature extraction, Deep learning, Image edge detection, Task analysis, Monitoring, Neurons, Fuses, Crack detection, deep learning BibRef

Chen, Y.[Yihan], Gu, X.Y.[Xing-Yu], Liu, Z.[Zhen], Liang, J.[Jia],
A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Ling, J.Y.[Jian-Yu], Qian, R.[Rongyi], Shang, K.[Ke], Guo, L.[Linyan], Zhao, Y.[Yu], Liu, D.[Dongyi],
Research on the Dynamic Monitoring Technology of Road Subgrades with Time-Lapse Full-Coverage 3D Ground Penetrating Radar (GPR),
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Daraghmi, Y.A.[Yousef-Awwad], Wu, T.H.[Tsung-Hsiang], Ik, T.U.[Tsě-Uí],
Crowdsourcing-Based Road Surface Evaluation and Indexing,
ITS(23), No. 5, May 2022, pp. 4164-4175.
IEEE DOI 2205
Roads, Rough surfaces, Surface roughness, Monitoring, Smart phones, Vibrations, Standards, Crowdsourcing, road roughness, power spectral density BibRef

Ahmed, A.[Adeel], Ashfaque, M.[Moeez], Ulhaq, M.U.[Muhammad Uzair], Mathavan, S.[Senthan], Kamal, K.[Khurram], Rahman, M.[Mujib],
Pothole 3D Reconstruction With a Novel Imaging System and Structure From Motion Techniques,
ITS(23), No. 5, May 2022, pp. 4685-4694.
IEEE DOI 2205
Cameras, Roads, Image reconstruction, Surface reconstruction, Measurement by laser beam, metrology BibRef

Chen, Z.P.[Zhi-Peng], Li, Q.Q.[Qing-Quan], Xue, W.X.[Wei-Xin], Zhang, D.[Dejin], Xiong, S.[Siting], Yin, Y.[Yu], Lv, S.[Shiwang],
Rapid Inspection of Large Concrete Floor Flatness Using Wheeled Robot with Aided-INS,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Yuan, G.[Genji], Li, J.B.[Jian-Bo], Meng, X.L.[Xiang-Long], Li, Y.[Yinong],
CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework,
IET-ITS(16), No. 6, 2022, pp. 782-799.
DOI Link 2205
BibRef

Park, M.J.[Min Jae], Kim, J.[Jihyung], Jeong, S.[Sanggi], Jang, A.[Arum], Bae, J.[Jaehoon], Ju, Y.K.[Young K.],
Machine Learning-Based Concrete Crack Depth Prediction Using Thermal Images Taken under Daylight Conditions,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Schladitz, K.[Katja],
Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images,
PR(129), 2022, pp. 108747.
Elsevier DOI 2206
Computed tomography, Fractional Brownian surface, 3d segmentation, Crack detection, Machine learning, Deep learning BibRef

Khadka, R.[Rajiv], Acharya, M.[Mahesh], LaBrier, D.[Daniel], Mashal, M.[Mustafa],
Visualization of Macroscopic Structure of Ultra-high Performance Concrete Based on X-ray Computed Tomography Using Immersive Environments,
VAMR22(I:20-33).
Springer DOI 2206
BibRef

Vassilev, V.[Vessen],
Road Surface Recognition at mm-Wavelengths Using a Polarimetric Radar,
ITS(23), No. 7, July 2022, pp. 6985-6990.
IEEE DOI 2207
Surface roughness, Rough surfaces, Ice, Coherence, Scattering parameters, Radar, Radar polarimetry, Radar polarimetry, target entropy BibRef

Tang, W.H.[Wen-Hao], Huang, S.[Sheng], Zhao, Q.M.[Qi-Ming], Li, R.[Ren], Huangfu, L.[Luwen],
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection,
ITS(23), No. 7, July 2022, pp. 8652-8661.
IEEE DOI 2207
Diseases, Task analysis, Roads, Object detection, Feature extraction, Image segmentation, Image resolution, object localization BibRef

Sun, M.[Meng], Pan, J.J.[Jing-Jing], Wang, Y.[Yide], Zhang, X.F.[Xiao-Fei], Xiao, X.T.[Xiao-Ting], Fauchard, C.[Cyrille], Bastard, C.L.[Cédric Le],
Time-Delay Estimation by Enhanced Orthogonal Matching Pursuit Method for Thin Asphalt Pavement With Similar Permittivity,
ITS(23), No. 7, July 2022, pp. 8940-8948.
IEEE DOI 2207
Asphalt, Permittivity, Matching pursuit algorithms, Estimation, Media, Matrix decomposition, Task analysis, Pavement survey, orthogonal matching pursuit (OMP) BibRef

Kun, J.[Ji], Zhenhai, Z.[Zhang], Jiale, Y.[Yu], Jianwu, D.[Dang],
A deep learning-based method for pixel-level crack detection on concrete bridges,
IET-IPR(16), No. 10, 2022, pp. 2609-2622.
DOI Link 2207
BibRef

Zhang, B.[Bin], Zhao, H.[Hua], Tan, C.J.[Cheng-Jun], OBrien, E.J.[Eugene J.], Fitzgerald, P.C.[Paul C.], Kim, C.W.[Chul-Woo],
Laboratory Investigation on Detecting Bridge Scour Using the Indirect Measurement from a Passing Vehicle,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, Y.[Yi], Fan, J.F.[Jun-Fu], Zhang, M.Z.[Meng-Zhen], Shi, Z.W.[Zong-Wen], Liu, R.F.[Ru-Fei], Guo, B.[Bing],
A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Souriou, D.[David], Kadkhodazadeh, S.[Sima], Dérobert, X.[Xavier], Guilbert, D.[David], Ihamouten, A.[Amine],
Experimental Parametric Study of a Functional-Magnetic Material Designed for the Monitoring of Corrosion in Reinforced Concrete Structures,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef


Lank, M.[Martin], Friedjungová, M.[Magda],
Road Quality Classification,
CIAP22(II:553-563).
Springer DOI 2205
BibRef

Vojir, T.[Tomas], Šipka, T.[Tomáš], Aljundi, R.[Rahaf], Chumerin, N.[Nikolay], Reino, D.O.[Daniel Olmeda], Matas, J.[Jiri],
Road Anomaly Detection by Partial Image Reconstruction with Segmentation Coupling,
ICCV21(15631-15640)
IEEE DOI 2203
Couplings, Training, Surface reconstruction, Roads, Semantics, Transform coding, Tires, Scene analysis and understanding BibRef

Nayyeri, F.[Fereshteh], Zhou, J.[Jun],
Multi-Resolution ResNet for Road and Bridge Crack Detection,
DICTA21(1-8)
IEEE DOI 2201
Training, Bridges, Image resolution, Computational modeling, Roads, Image edge detection, Digital images, crack detection, ResNet, crack dataset BibRef

Shahbazi, L.[Leila], Majidi, B.[Babak], Movaghar, A.[Ali],
Autonomous Road Pavement Inspection and Defect Analysis for Smart City Maintenance,
IPRIA21(1-5)
IEEE DOI 2201
Deep learning, Visualization, Asphalt, Smart cities, Roads, Inspection, Safety, Road pavement crack detection, smart city BibRef

Duan, L.[Lijuan], Zeng, J.[Jun], Pang, J.[Junbiao], Wang, J.Z.[Jun-Zhe],
Pavement Crack Detection Using Multi-stage Structural Feature Extraction Model,
ICIP21(969-973)
IEEE DOI 2201
Training, Art, Roads, Maintenance engineering, Feature extraction, Complexity theory, crack detection, deep supervision, structural feature extraction BibRef

Zhang, Y.[Yujia], Li, Q.[Qianzhong], Zhao, X.G.[Xiao-Guang], Tan, M.[Min],
TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained Pavement Disease Segmentation,
WACV21(3654-3663)
IEEE DOI 2106
Convolution, Roads, Inspection, Maintenance engineering, Safety BibRef

Masihullah, S.[Shaik], Garg, R.[Ritu], Mukherjee, P.[Prerana], Ray, A.[Anupama],
Attention Based Coupled Framework for Road and Pothole Segmentation,
ICPR21(5812-5819)
IEEE DOI 2105
Training, Image segmentation, Visualization, Rain, Roads, Snow, Vehicle safety, Pothole Detection, Road Segmentation, Deep Networks BibRef

Wu, X.Y.[Xuan-Yi], Ma, J.F.[Jian-Fei], Sun, Y.[Yu], Zhao, C.[Chenqiu], Basu, A.[Anup],
Multi-Scale Deep Pixel Distribution Learning for Concrete Crack Detection,
ICPR21(6577-6583)
IEEE DOI 2105
Deep learning, Learning systems, Image segmentation, Feature extraction, Pattern recognition, Surface cracks, concrete crack detection BibRef

Inoue, Y.[Yuki], Nagayoshi, H.[Hiroto],
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors,
ICPR21(65-72)
IEEE DOI 2105
Annotations, Roads, Semantics, Buildings, Brightness, Detectors, Inspection BibRef

Chitale, P.A., Kekre, K.Y., Shenai, H.R., Karani, R., Gala, J.P.,
Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing,
IVCNZ20(1-6)
IEEE DOI 2012
Deep learning, Pandemics, Shape, Roads, Image processing, Estimation, Maintenance engineering, YOLO, Deep Learning, Dimension Estimation BibRef

Cannelle, B., Beltzung, F., Thiémard-Spada, M.,
Application of Photogrammetry and Image Processing for the Study Of Porous Surface Courses,
ISPRS20(B2:745-749).
DOI Link 2012
BibRef

Ravi, R., Bullock, D., Habib, A.,
Highway and Airport Runway Pavement Inspection Using Mobile Lidar,
ISPRS20(B1:349-354).
DOI Link 2012
BibRef

Pontoglio, E., Colucci, E., Lingua, A., Maschio, P., Migliazza, M.R., Scavia, C.,
UAV and Close-range Photogrammetry to Support Geo-mechanical Analysis In Safety Road Management: the 'Vallone d'Elva' Road,
ISPRS20(B2:1159-1166).
DOI Link 2012
BibRef

Pinto, L., Bianchini, F., Nova, V., Passoni, D.,
Low-cost UAS Photogrammetry for Road Infrastructures' Inspection,
ISPRS20(B2:1145-1150).
DOI Link 2012
BibRef

Ono, Y., Tsuji, A., Abe, J., Noguchi, H., Abe, J.,
Robust Detection of Surface Anomaly Using Lidar Point Cloud With Intensity,
ISPRS20(B2:1129-1136).
DOI Link 2012
BibRef

Shokri, P., Shahbazi, M., Lichti, D., Nielsen, J.,
Vision-based Approaches for Quantifying Cracks In Concrete Structures,
ISPRS20(B2:1167-1174).
DOI Link 2012
BibRef

Niu, B., Wu, H., Meng, Y.,
Application of CEM Algorithm in the Field of Tunnel Crack Identification,
ICIVC20(232-236)
IEEE DOI 2009
Feature extraction, Gabor filters, Road transportation, Filtering algorithms, Image edge detection, Inspection, CEM BibRef

Benz, C., Debus, P., Ha, H.K., Rodehorst, V.,
Crack Segmentation on UAS-based Imagery using Transfer Learning,
IVCNZ19(1-6)
IEEE DOI 2004
Code, Crack Detection.
WWW Link. autonomous aerial vehicles, convolutional neural nets, crack detection, image resolution, image segmentation, UAS BibRef

Park, J.S., Lee, K.S., Kim, S.,
Assessment for a Condition Using Terrestrial Lidar Data,
Gi4DM19(311-314).
DOI Link 1912
Potholes, etc. BibRef

d'Aranno, P., di Benedetto, A., Fiani, M., Marsella, M.,
Remote Sensing Technologies for Linear Infrastructure Monitoring,
GEORES19(461-468).
DOI Link 1912
E.g. roads. BibRef

Liebold, F., Maas, H.G., Heravi, A.A.,
Crack Width Measurement for Non-planar Surfaces By Triangle Mesh Analysis in Civil Engineering Material Testing,
Optical3D19(107-113).
DOI Link 1912
BibRef

Seydi, S.T., Rastiveis, H.,
A Deep Learning Framework for Roads Network Damage Assessment Using Post-earthquake Lidar Data,
SMPR19(955-961).
DOI Link 1912
BibRef

Fakhri, S.A., Fakhri, S.A., Saadatseresht, M.,
Road Crack Detection Using Gaussian/prewitt Filter,
SMPR19(371-377).
DOI Link 1912
BibRef

Truong-Hong, L., Laefer, D.F., Lindenbergh, R.C.,
Automatic Detection of Road Edges From Aerial Laser Scanning Data,
Laser19(1135-1140).
DOI Link 1912
BibRef

van der Horst, B.B., Lindenbergh, R.C., Puister, S.W.J.,
Mobile Laser Scan Data for Road Surface Damage Detection,
Laser19(1141-1148).
DOI Link 1912
BibRef

König, J., Jenkins, M.D.[M. David], Barrie, P., Mannion, M., Morison, G.,
A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating,
ICIP19(1460-1464)
IEEE DOI 1910
Semantic Segmentation, Attention, Residual Connections, U-Net, Surface Cracks BibRef

Dhiman, A., Chien, H., Klette, R.,
Road surface distress detection in disparity space,
IVCNZ17(1-6)
IEEE DOI 1902
road accidents, road traffic, roads, stereo image processing, road surface distress detection, traffic accidents, Sensors BibRef

Yang, L., Li, B., Li, W., Jiang, B., Xiao, J.,
Semantic Metric 3D Reconstruction for Concrete Inspection,
Odometry18(1624-16248)
IEEE DOI 1812
Inspection, Semantics, Measurement, Visualization, Simultaneous localization and mapping, Image segmentation BibRef

Song, W., Workman, S., Hadzic, A., Zhang, X., Green, E., Chen, M., Souleyrette, R., Jacobs, N.,
FARSA: Fully Automated Roadway Safety Assessment,
WACV18(521-529)
IEEE DOI 1806
image processing, neural nets, road safety, roads, traffic engineering computing, FARSA, US Road Assessment Program, Training BibRef

Liu, X.Z.[Xiang-Zeng], Ai, Y.F.[Yun-Feng], Scherer, S.[Sebastian],
Robust image-based crack detection in concrete structure using multi-scale enhancement and visual features,
ICIP17(2304-2308)
IEEE DOI 1803
Indexes, Crack detection, concrete structure, guided filter, image enhancement BibRef

Güldür Erkal, B., Apaydin, N.M.,
Bridge Surface Damage Detection Application with A Laser-based Software Prototype,
GeoAdvances17(55-57).
DOI Link 1805
BibRef

Grünauer, A.[Andreas], Halmetschlager-Funek, G.[Georg], Prankl, J.[Johann], Vincze, M.[Markus],
Learning the Floor Type for Automated Detection of Dirt Spots for Robotic Floor Cleaning Using Gaussian Mixture Models,
CVS17(576-589).
Springer DOI 1711
BibRef

Chaudhury, S., Nakano, G., Takada, J., Iketani, A.,
Spatial-Temporal Motion Field Analysis for Pixelwise Crack Detection on Concrete Surfaces,
WACV17(336-344)
IEEE DOI 1609
Bridges, Concrete, Labeling, Loading, Maintenance engineering, Safety, Surface cracks. BibRef

Martínez-Sánchez, J., Puente, I., GonzálezJorge, H., Riveiro, B., Arias, P.,
Automatic Thickness And Volume Estimation Of Sprayed Concrete On Anchored Retaining Walls From Terrestrial Lidar Data,
ISPRS16(B5: 521-526).
DOI Link 1610
BibRef

Abdic, I., Fridman, L., Brown, D.E., Angell, W., Reimer, B., Marchi, E., Schuller, B.,
Detecting road surface wetness from audio: A deep learning approach,
ICPR16(3458-3463)
IEEE DOI 1705
Cameras, Data collection, Recurrent neural networks, Roads, Rough surfaces, Spectrogram, Tires BibRef

Vandoni, J., Le Hégarat-Mascle, S., Aldea, E.,
Crack detection based on a Marked Point Process model,
ICPR16(3933-3938)
IEEE DOI 1705
Adaptation models, Data models, Extremities, Image segmentation, Joining processes, Roads, Robustness BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Paint and Printing Quality, Print Analysis .


Last update:Aug 14, 2022 at 21:20:19