23.1.8 Power Line Extraction, Powerline Extraction, Radar, SAR, Lidar, Laser, Depth

Chapter Contents (Back)
Power Line Detection. Aerial Image Analysis. SAR. Radar.
See also Transmission Towers, Pylons, Poles, Extraction, Radar, SAR, Lidar, Laser, Depth.

Jones, D.I.,
Aerial inspection of overhead power lines using video: estimation of image blurring due to vehicle and camera motion,
VISP(147), No. 2, April 2000, pp. 157. 0005

Golightly, I.[Ian], Jones, D.[Dewi],
Corner detection and matching for visual tracking during power line inspection,
IVC(21), No. 9, September 2003, pp. 827-840.
Elsevier DOI 0308

Sun, C.M.[Chang-Ming], Jones, R.[Ronald], Talbot, H.[Hugues], Wu, X.L.[Xiao-Liang], Cheong, K.[Kevin], Beare, R.[Richard], Buckley, M.[Michael], Berman, M.[Mark],
Measuring the distance of vegetation from powerlines using stereo vision,
PandRS(60), No. 4, June 2006, pp. 269-283.
Elsevier DOI
PDF File. 0610
Stereo matching; powerline inspection; power pole segmentation; vegetation clearance; 3D vegetation surface BibRef

Li, Z.R.[Zheng-Rong], Liu, Y.[Yuee], Walker, R.[Rodney], Hayward, R.[Ross], Zhang, J.L.[Jing-Lan],
Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform,
MVA(21), No. 5, August 2010, pp. 677-686.
WWW Link. 1011

Liu, Y.[Yuee], Li, Z.R.[Zheng-Rong], Hayward, R.[Ross], Walker, R.[Rodney], Jin, H.[Hang],
Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors,

Li, Z.R.[Zheng-Rong], Liu, Y.[Yuee], Hayward, R.[Ross], Zhang, J.L.[Jing-Lan], Cai, J.H.[Jin-Hai],
Knowledge-based power line detection for UAV surveillance and inspection systems,

Mills, S.J., Gerardo Castro, M.P., Li, Z., Cai, J., Hayward, R., Mejias, L., Walker, R.A.,
Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors,
GeoRS(48), No. 9, September 2010, pp. 3379-3390.

Li, Z.R.[Zheng-Rong], Hayward, R., Zhang, J.L.[Jing-Lan], Liu, Y.[Yuee],
Individual Tree Crown Delineation Techniques for Vegetation Management in Power Line Corridor,

Ma, Q., Goshi, D.S., Shih, Y.C., Sun, M.T.,
An Algorithm for Power Line Detection and Warning Based on a Millimeter-Wave Radar Video,
IP(20), No. 12, December 2011, pp. 3534-3543.

Zhu, L.L.[Ling-Li], Hyyppä, J.[Juha],
Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas,
RS(6), No. 11, 2014, pp. 11267-11282.
DOI Link 1412

See also Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D, The. BibRef

Cheng, L.[Liang], Tong, L.H.[Li-Hua], Wang, Y.[Yu], Li, M.C.[Man-Chun],
Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data,
RS(6), No. 4, 2014, pp. 3302-3320.
DOI Link 1405

See also Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis. BibRef

Guo, B.[Bo], Huang, X.F.[Xian-Feng], Zhang, F.[Fan], Sohn, G.[Gunho],
Classification of airborne laser scanning data using JointBoost,
PandRS(100), No. 1, 2015, pp. 71-83.
Elsevier DOI 1502
LiDAR BibRef

Sohn, G.[Gunho], Jwa, Y.[Yoonseok], Kim, H.B.[Heungsik Brian],
Automatic Powerline Scene Classification And Reconstruction Using Airborne Lidar Data,
AnnalsPRS(I-3), No. 2012, pp. 167-172.
DOI Link 1209
Earlier: A2, A1, A3:
Automatic 3D Powerline Reconstruction Using Airborne LIDAR Data,
Laser09(105). 0909

See also Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data. BibRef

Luo, C., Sohn, G.,
Line-based Classification of Terrestrial Laser Scanning Data using Conditional Random Field,
DOI Link 1402

Kim, H.B.[Heungsik Brian], Sohn, G.[Gunho],
Random Forests Based Multiple Classifier System For Power-Line Scene Classification,
DOI Link 1109
3D classification of power-line scene from airborne laser scanning data using Random Forests,
PDF File. 1009

Jwa, Y.[Yoonseok], Sohn, G.[Gunho],
A multi-level span analysis for improving 3D power-line reconstruction performance using airborne laser scanning data,
PDF File. 1009

Cho, C.J., Ko, H.,
Video-Based Dynamic Stagger Measurement of Railway Overhead Power Lines Using Rotation-Invariant Feature Matching,
ITS(16), No. 3, June 2015, pp. 1294-1304.
Feature extraction BibRef

Chen, Y.P.[Yun-Ping], Li, Y.[Yang], Zhang, H.X.[Hui-Xiong], Tong, L.[Ling], Cao, Y.X.[Yong-Xing], Xue, Z.H.[Zhi-Hang],
Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform,
PR(49), No. 1, 2016, pp. 174-186.
Elsevier DOI 1511
High resolution BibRef

Guo, B.[Bo], Li, Q.Q.[Qing-Quan], Huang, X.F.[Xian-Feng], Wang, C.S.[Chi-Sheng],
An Improved Method for Power-Line Reconstruction from Point Cloud Data,
RS(8), No. 1, 2016, pp. 36.
DOI Link 1602

Matikainen, L.[Leena], Lehtomäki, M.[Matti], Ahokas, E.[Eero], Hyyppä, J.[Juha], Karjalainen, M.[Mika], Jaakkola, A.[Anttoni], Kukko, A.[Antero], Heinonen, T.[Tero],
Remote sensing methods for power line corridor surveys,
PandRS(119), No. 1, 2016, pp. 10-31.
Elsevier DOI 1610
Power line BibRef

Pastucha, E.[Elzbieta],
Catenary System Detection, Localization and Classification Using Mobile Scanning Data,
RS(8), No. 10, 2016, pp. 801.
DOI Link 1609
overhead power lines. BibRef

Jung, J.W.[Jae-Wook], Chen, L.[Leihan], Sohn, G.[Gunho], Luo, C.[Chao], Won, J.U.[Jong-Un],
Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data,
RS(8), No. 12, 2016, pp. 1008.
DOI Link 1612

Zhang, Y.[Yong], Yuan, X.X.[Xiu-Xiao], Fang, Y.[Yi], Chen, S.Y.[Shi-Yu],
UAV Low Altitude Photogrammetry for Power Line Inspection,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702

Zhang, Y.[Yong], Yuan, X.X.[Xiu-Xiao], Li, W.Z.[Wen-Zhuo], Chen, S.Y.[Shi-Yu],
Automatic Power Line Inspection Using UAV Images,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Jaw, Y.[Yoonseok], Sohn, G.[Gunho],
Wind adaptive modeling of transmission lines using minimum description length,
PandRS(125), No. 1, 2017, pp. 193-206.
Elsevier DOI 1703
3D transmission line model BibRef

Jiang, S.[San], Jiang, W.[Wanshou], Huang, W.[Wei], Yang, L.[Liang],
UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Jiang, S.[San], Jiang, W.[Wanshou],
On-Board GNSS/IMU Assisted Feature Extraction and Matching for Oblique UAV Images,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Qin, X.Y.[Xin-Yan], Wu, G.P.[Gong-Ping], Ye, X.[Xuhui], Huang, L.[Le], Lei, J.[Jin],
A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Wang, Y.J.[Yan-Jun], Chen, Q.[Qi], Liu, L.[Lin], Zheng, D.Y.[Dun-Yong], Li, C.K.[Chao-Kui], Li, K.[Kai],
Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Chen, C.[Chi], Yang, B.S.[Bi-Sheng], Song, S.[Shuang], Peng, X.Y.[Xiang-Yang], Huang, R.G.[Rong-Gang],
Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

Yang, B.S.[Bi-Sheng], Chen, C.[Chi],
Automatic registration of UAV-borne sequent images and LiDAR data,
PandRS(101), No. 1, 2015, pp. 262-274.
Elsevier DOI 1503
Unmanned aerial vehicles mapping BibRef

Wang, Y.J.[Yan-Jun], Chen, Q.[Qi], Liu, L.[Lin], Li, X.[Xiong], Sangaiah, A.K.[Arun Kumar], Li, K.[Kai],
Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Zhou, R.Q.[Ru-Qin], Jiang, W.S.[Wan-Shou], Jiang, S.[San],
A Novel Method for High-Voltage Bundle Conductor Reconstruction from Airborne LiDAR Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Ortega, S.[Sebastián], Trujillo, A.[Agustín], Santana, J.M.[José Miguel], Suárez, J.P.[José Pablo], Santana, J.[Jaisiel],
Characterization and modeling of power line corridor elements from LiDAR point clouds,
PandRS(152), 2019, pp. 24-33.
Elsevier DOI 1905
Power line, Classification, Point cloud, Modeling BibRef

Zhang, H.[Heng], Yang, W.[Wen], Yu, H.[Huai], Zhang, H.[Haijian], Xia, G.S.[Gui-Song],
Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906

Peng, S.[Shuwen], Xi, X.H.[Xiao-Huan], Wang, C.[Cheng], Dong, P.L.[Pin-Liang], Wang, P.[Pu], Nie, S.[Sheng],
Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Sánchez-Rodríguez, A.[Ana], Soilán, M.[Mario], Cabaleiro, M.[Manuel], Arias, P.[Pedro],
Automated Inspection of Railway Tunnels' Power Line Using LiDAR Point Clouds,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Tomaszewski, M.[Michal], Michalski, P.[Pawel], Ruszczak, B.[Bogdan], Zator, S.[Slawomir],
Detection of power line insulators on digital images with the use of laser spots,
IET-IPR(13), No. 12, October 2019, pp. 2358-2366.
DOI Link 1911

Zhang, R.Z.[Rui-Zhuo], Yang, B.S.[Bi-Sheng], Xiao, W.[Wen], Liang, F.[Fuxun], Liu, Y.[Yang], Wang, Z.M.[Zi-Ming],
Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

Tao, X.[Xian], Zhang, D.P.[Da-Peng], Wang, Z.H.[Zi-Hao], Liu, X.L.[Xi-Long], Zhang, H.Y.[Hong-Yan], Xu, D.[De],
Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks,
SMCS(50), No. 4, April 2020, pp. 1486-1498.
Insulators, Inspection, Feature extraction, Object detection, Shape, Support vector machines, Convolutional neural networks, insulators BibRef

Jung, J.[Jaehoon], Che, E.[Erzhuo], Olsen, M.J.[Michael J.], Shafer, K.C.[Katherine C.],
Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach,
PandRS(163), 2020, pp. 343-361.
Elsevier DOI 2005
Powerlines, Lidar, Laser scanning, Point cloud, Voxel-based subsampling BibRef

Munir, N.[Nosheen], Awrangjeb, M.[Mohammad], Stantic, B.[Bela],
Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Pastucha, E.[Elzbieta], Puniach, E.[Edyta], Scislowicz, A.[Agnieszka], Cwiakala, P.[Pawel], Niewiem, W.[Witold], Wiacek, P.[Pawel],
3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Nardinocchi, C., Balsi, M., Esposito, S.,
Fully Automatic Point Cloud Analysis for Powerline Corridor Mapping,
GeoRS(58), No. 12, December 2020, pp. 8637-8648.
Wires, Poles and towers, Laser radar, Task analysis, Inspection, Vegetation mapping, Data analysis, unmanned aerial vehicles (UAVs) BibRef

Ma, Y.P.[Yun-Peng], Li, Q.W.[Qing-Wu], Chu, L.[Lulu], Zhou, Y.Q.[Ya-Qin], Xu, C.[Chang],
Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Fan, Y.Z.[Yong-Zhao], Zou, R.[Rong], Fan, X.Y.[Xiao-Yun], Dong, R.D.[Ren-Dong], Xie, M.Y.[Meng-You],
A Hierarchical Clustering Method to Repair Gaps in Point Clouds of Powerline Corridor for Powerline Extraction,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104

Siranec, M.[Marek], Höger, M.[Marek], Otcenasova, A.[Alena],
Advanced Power Line Diagnostics Using Point Cloud Data: Possible Applications and Limits,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105

Li, B.[Bo], Chen, C.[Cheng], Dong, S.[Shiwen], Qiao, J.F.[Jun-Feng],
Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks,
SP:IC(96), 2021, pp. 116278.
Elsevier DOI 2106
Transmission line detection, Deep neural networks, Convolutional neural networks BibRef

Luo, Y.H.[Yan-Hong], Yu, X.[Xue], Yang, D.S.[Dong-Sheng],
A new recognition algorithm for high-voltage lines based on improved LSD and convolutional neural networks,
IET-IPR(15), No. 1, 2021, pp. 260-268.
DOI Link 2106

Tan, J.X.[Jun-Xiang], Zhao, H.J.[Hao-Jie], Yang, R.H.[Rong-Hao], Liu, H.[Hua], Li, S.[Shaoda], Liu, J.F.[Jian-Fei],
An Entropy-Weighting Method for Efficient Power-Line Feature Evaluation and Extraction from LiDAR Point Clouds,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Dutta, T.[Tanima], Soni, A.[Aishwarya], Gona, P.[Prateek], Gupta, H.P.[Hari Prabhat],
Real Testbed for Autonomous Anomaly Detection in Power Grid Using Low-Cost Unmanned Aerial Vehicles and Aerial Imaging,
MultMedMag(28), No. 3, July 2021, pp. 63-74.
Cameras, Estimation, Power grids, Anomaly detection, Monitoring, Lenses, 3D Anomaly Detection, Power Grid Monitoring BibRef

Chen, W.X.[Wen-Xiang], Li, Y.N.[Ying-Na], Zhao, Z.G.[Zhen-Gang],
InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110

Huang, W.[Wei], Jiang, S.[San], He, S.[Sheng], Jiang, W.S.[Wan-Shou],
Accelerated Multi-View Stereo for 3D Reconstruction of Transmission Corridor with Fine-Scale Power Line,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110

Bao, W.X.[Wen-Xia], Ren, Y.X.[Yang-Xun], Wang, N.[Nian], Hu, G.S.[Gen-Sheng], Yang, X.J.[Xian-Jun],
Detection of Abnormal Vibration Dampers on Transmission Lines in UAV Remote Sensing Images with PMA-YOLO,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110

Mongus, D.[Domen], Brumen, M.[Matej], Žlaus, D.[Danijel], Kohek, Š.[Štefan], Tomažic, R.[Roman], Kerin, U.[Uroš], Kolmanic, S.[Simon],
A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112

Liu, X.Y.[Xin-Yu], Jin, Z.H.[Zhi-Heng], Jiang, H.[Hao], Miao, X.R.[Xi-Ren], Chen, J.[Jing], Lin, Z.C.[Zhi-Cheng],
Quality assessment for inspection images of power lines based on spatial and sharpness evaluation,
IET-IPR(16), No. 2, 2022, pp. 356-364.
DOI Link 2201

Zhong, J.P.[Jun-Ping], Liu, Z.G.[Zhi-Gang], Yang, C.[Cheng], Wang, H.R.[Hong-Rui], Gao, S.B.[Shi-Bin], Núñez, A.[Alfredo],
Adversarial Reconstruction Based on Tighter Oriented Localization for Catenary Insulator Defect Detection in High-Speed Railways,
ITS(23), No. 2, February 2022, pp. 1109-1120.
Insulators, Rail transportation, Feature extraction, Inspection, Image reconstruction, Machine learning, Lighting, high-speed railways BibRef

Zhao, L.[Le], Yao, H.[Hongtai], Tian, M.[Meng], Wang, X.[Xianpei],
Robust power line extraction from aerial image using object-based Gaussian-Markov random field with gravity property parameters,
SP:IC(103), 2022, pp. 116634.
Elsevier DOI 2203
Aerial image, Power line extraction, Object-based Markov random field, Gaussian-Markov model, Gravity property model BibRef

Ma, B.[Bin], Fu, Y.K.[Yong-Kang], Wang, C.[Chunpeng], Li, J.[Jian], Wang, Y.[Yuli],
A high-performance insulators location scheme based on YOLOv4 deep learning network with GDIoU loss function,
IET-IPR(16), No. 4, 2022, pp. 1124-1134.
DOI Link 2203

Zhao, W.[Wenbo], Dong, Q.[Qing], Zuo, Z.L.[Zheng-Li],
A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Xu, T.[Tao], Gao, X.J.[Xian-Jun], Yang, Y.[Yuanwei], Xu, L.[Lei], Xu, J.[Jie], Wang, Y.J.[Yan-Jun],
Construction of a Semantic Segmentation Network for the Overhead Catenary System Point Cloud Based on Multi-Scale Feature Fusion,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206

Geng, Y.X.[Yi-Xuan], Pan, F.J.[Feng-Jun], Jia, L.M.[Li-Min], Wang, Z.P.[Zhi-Peng], Qin, Y.[Yong], Tong, L.[Lei], Li, S.Q.[Shi-Qi],
UAV-LiDAR-Based Measuring Framework for Height and Stagger of High-Speed Railway Contact Wire,
ITS(23), No. 7, July 2022, pp. 7587-7600.
Wires, Laser radar, Rail transportation, Rails, Vehicle dynamics, Position measurement, Contact wire, height, LiDAR, point clouds, UAV BibRef

Yu, L.[Long], Gao, S.B.[Shi-Bin], Zhang, D.K.[Dong-Kai], Kang, G.Q.[Gao-Qiang], Zhan, D.[Dong], Roberts, C.[Clive],
A Survey on Automatic Inspections of Overhead Contact Lines by Computer Vision,
ITS(23), No. 8, August 2022, pp. 10104-10125.
Inspection, Wires, Contacts, Rail transportation, Force measurement, Force, Overhead contact lines, computer vision, measurement, deep learning BibRef

Zhou, Y.[Yaqin], Xu, C.[Chang], Dai, Y.F.[Yun-Feng], Feng, X.M.[Xing-Ming], Ma, Y.P.[Yun-Peng], Li, Q.W.[Qing-Wu],
Dual-View Stereovision-Guided Automatic Inspection System for Overhead Transmission Line Corridor,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Zou, K.S.[Kuan-Sheng], Jiang, Z.B.[Zhen-Bang],
Power Line Extraction Framework Based on Edge Structure and Scene Constraints,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Zhang, L.[Lele], Wang, J.[Jinhu], Shen, Y.[Yueqian], Liang, J.[Jian], Chen, Y.Y.[Yu-Yu], Chen, L.S.[Lin-Sheng], Zhou, M.[Mei],
A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Bao, W.X.[Wen-Xia], Du, X.[Xiang], Wang, N.[Nian], Yuan, M.[Mu], Yang, X.J.[Xian-Jun],
A Defect Detection Method Based on BC-YOLO for Transmission Line Components in UAV Remote Sensing Images,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Zhang, S.Y.[Sheng-Yuan], Meng, Q.X.[Qing-Xiang], Hu, Y.L.[Yu-Long], Fu, Z.L.[Zhong-Liang], Chen, L.[Lijin],
A Method for the Automatic Extraction of Support Devices in an Overhead Catenary System Based on MLS Point Clouds,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Maskeliunas, R.[Rytis], Pomarnacki, R.[Raimondas], Huynh, V.K.[Van Khang], Damaševicius, R.[Robertas], Plonis, D.[Darius],
Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301

Gauce, D.[Diana], Lektauers, A.[Arnis], Solovjova, I.[Irina], Grants, R.[Roberts], Kolosovs, D.[Deniss], Litvinenko, A.[Anna],
Application of Digital Twin in Medium-Voltage Overhead Distribution Network Inspection,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301

Li, Z.[Ziran], Zhang, Y.[Yanwen], Wu, H.[Hao], Suzuki, S.[Satoshi], Namiki, A.[Akio], Wang, W.[Wei],
Design and Application of a UAV Autonomous Inspection System for High-Voltage Power Transmission Lines,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302

Munir, N.[Nosheen], Awrangjeb, M.[Mohammad], Stantic, B.[Bela],
Power Line Extraction and Reconstruction Methods from Laser Scanning Data: A Literature Review,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Wang, J.Y.[Jin-Yu], Li, Y.N.[Ying-Na], Chen, W.X.[Wen-Xiang],
UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303

Zhang, Y.Z.[Yun-Zuo], Song, Z.C.[Zhou-Chen], Li, W.[Wenbo],
Enhancement multi-module network for few-shot leaky cable fixture detection in railway tunnel,
SP:IC(113), 2023, pp. 116943.
Elsevier DOI 2303
Leaky cable fixture detection, Few-shot learning, Enhancement multi-module network, Weight balance coefficient BibRef

Lee, S.[Seulbi], Ham, Y.[Youngjib],
Measuring the Distance between Trees and Power Lines under Wind Loads to Assess the Heightened Potential Risk of Wildfire,
RS(15), No. 6, 2023, pp. 1485.
DOI Link 2304

Hu, J.W.[Jing-Wei], He, J.[Jing], Guo, C.J.[Cheng-Jun],
End-to-End Powerline Detection Based on Images from UAVs,
RS(15), No. 6, 2023, pp. 1570.
DOI Link 2304

Tang, M.[Minan], Liang, K.[Kai], Qiu, J.[Jiandong],
Small insulator target detection based on multi-feature fusion,
IET-IPR(17), No. 5, 2023, pp. 1520-1533.
DOI Link 2304
feature extraction, flaw detection, insulators, support vector machines BibRef

Xu, C.[Chang], Li, Q.W.[Qing-Wu], Jiang, X.B.[Xiong-Biao], Yu, D.B.[Da-Bing], Zhou, Y.[Yaqin],
Dual-Space Graph-Based Interaction Network for RGB-Thermal Semantic Segmentation in Electric Power Scene,
CirSysVideo(33), No. 4, April 2023, pp. 1577-1592.
Feature extraction, Semantics, Visualization, Data mining, Power systems, Inspection, Image edge detection, RGB-T, scene comprehension BibRef

Li, Y.[Yong], Wei, S.D.[Shi-Di], Liu, X.[Xuan], Luo, Y.Z.[Yin-Zheng], Li, Y.F.[Ya-Feng], Shuang, F.[Feng],
An Improved Insulator and Spacer Detection Algorithm Based on Dual Network and SSD,
IEICE(E106-D), No. 5, May 2023, pp. 662-672.
WWW Link. 2305

Song, J.[Jiang], Qian, J.G.[Jian-Guo], Liu, Z.J.[Zheng-Jun], Jiao, Y.[Yang], Zhou, J.[Jiahui], Li, Y.R.[Yong-Rong], Chen, Y.M.[Yi-Ming], Guo, J.[Jie], Wang, Z.Q.[Zhi-Qiang],
Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306

Patsiouras, E.[Emmanouil], Mygdalis, V.[Vasileios], Pitas, I.[Ioannis],
Whitening Transformation inspired Self-Attention for Powerline Element Detection,
Visualization, Helicopters, Object detection, Machine learning, Inspection, Transformers, powerline inspection BibRef

Jain, A.[Arpit], Shah, T.[Tapan], Yousefhussien, M.[Mohammed], Pandey, A.[Achalesh],
Combining Remotely Sensed Imagery with Survival Models for Outage Risk Estimation of the Power Grid,
Schedules, Satellites, Vegetation mapping, Vegetation, Forestry, Predictive models, Power grids BibRef

Yu, W., Xi, J., Wu, Z., Lei, W., Zhu, C., Tang, T.,
A Method for Extracting Substation Equipment Based on UAV Laser Scanning Point Clouds,
DOI Link 2012

Kähler, O., Hochstöger, S., Kemper, G., Birchbauer, J.,
Automating Powerline Inspection: A Novel Multisensor System for Data Analysis Using Deep Learning,
DOI Link 2012

Wu, Z., Lei, W., Sun, W., Chen, C., Wang, H., Yu, W., Zhong, H., Zhu, C., Xi, J., Yang, B.,
Power Transmission Line Reconstruction From Sequential Oblique UAV Images,
DOI Link 2012

Choi, H., Koo, G., Kim, B.J., Kim, S.W.[S. Woo],
Real-time Power Line Detection Network using Visible Light and Infrared Images,
convolutional neural nets, image fusion, image segmentation, infrared imaging, inspection, learning (artificial intelligence), Real-time BibRef

Jiang, S., Jiang, W.,
UAV-based Oblique Photogrammetry for 3d Reconstruction of Transmission Line: Practices and Applications,
DOI Link 1912

Toschi, I., Morabito, D., Grilli, E., Remondino, F., Carlevaro, C., Cappellotto, A., Tamagni, G., Maffeis, M.,
Cloud-based Solution for Nationwide Power Line Mapping,
DOI Link 1912

Pu, S., Xie, L., Ji, M., Zhao, Y., Liu, W., Wang, L., Zhao, Y., Yang, F., Qiu, D.,
Real-time Powerline Corridor Inspection By Edge Computing of UAV Lidar Data,
DOI Link 1912

Yermo, M., Martínez, J., Lorenzo, O.G., Vilariño, D.L., Cabaleiro, J.C., Pena, T.F., Rivera, F.F.,
Automatic Detection and Characterisation of Power Lines and Their Surroundings Using Lidar Data,
DOI Link 1912

Ganovelli, F., Malomo, L., Scopigno, R.,
Reconstructing Power Lines from Images,
Wires, Cameras, Image segmentation, Image reconstruction, Image edge detection, power lines BibRef

Gubbi, J.[Jayavardhana], Varghese, A.[Ashley], Balamuralidhar, P.,
A new deep learning architecture for detection of long linear infrastructure,
DOI Link 1708
Adaptation models, Data models, Drones, Feature extraction, Machine learning, Monitoring, Training BibRef

Zhou, X.[Xiao], Zheng, X.L.[Xiao-Liang], Ou, K.[Kejun],
Power line detect system based on stereo vision and FPGA,
Decision making, Digital signal processing, Field programmable gate arrays, Image edge detection, Real-time systems, Robustness, Shape, FPGA, census transform, morphology, power line detect, real time, stereo, vision BibRef

Baker, L., Mills, S., Langlotz, T., Rathbone, C.,
Power line detection using Hough transform and line tracing techniques,
Cameras BibRef

Han, G.[Ge], Gong, W.[Wei], Cui, X.H.[Xiao-Hui], Zhang, M.[Miao], Chen, J.[Jun],
Estimation Of Insulator Contaminations By Means Of Remote Sensing Technique,
ISPRS16(B8: 73-77).
DOI Link 1610
To prevent power failures. BibRef

Zhou, G., Yuan, J., Yen, I.L., Bastani, F.,
Robust real-time UAV based power line detection and tracking,
Cameras BibRef

Li, B.L.[Bing-Lin], Thomas, G.[Gabriel], Williams, D.[Dexter],
Ice Detection on Electrical Power Cables,
ISVC15(II: 355-364).
Springer DOI 1601

Józków, G., Vander Jagt, B., Toth, C.,
Experiments with UAS Imagery for Automatic Modeling of Power Line 3D Geometry,
DOI Link 1512

Pan, W.W., Dou, Y.J., Wang, G.L., Wu, M.X., Ren, R.G., Xu, X.,
Development and Test of Blimp-Based Compact LIDAR Powewr-Line Inspection System,
DOI Link 1504

Cerón, A.[Alexander], Mondragón Bernal, I.F.[Iván F.], Prieto, F.[Flavio],
Towards Visual Based Navigation with Power Line Detection,
ISVC14(I: 827-836).
Springer DOI 1501

Liu, Y., Mejias, L., Li, Z.,
Fast Power Line Detection and Localization Using Steerable Filter For Active Uav Guidance,
DOI Link 1209

Liang, J.[Jing], Zhang, J.X.[Ji-Xian], Deng, K.Z.[Ka-Zhong], Liu, Z.J.[Zheng-Jun], Shi, Q.S.[Qun-Shan],
A New Power-Line Extraction Method Based on Airborne LiDAR Point Cloud Data,

Shi, Q.S.[Qun-Shan], Xu, Q.[Qing], Xing, S.[Shuai], Lan, C.Z.[Chao-Zhen], Liang, J.[Jing],
A Vector Collection Method Based on LiDAR Point Cloud Data,

Wan, X.[Xue], Qu, X.Z.[Xiao-Zhi], Wang, L.T.[Li-Tao], Wu, B.[Buwei], Zhang, J.Q.[Jian-Qing], Zheng, S.Y.[Shun-Yi],
Photogrammetric techniques for power line ranging,
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Candamo, J.[Joshua], Goldgof, D.[Dmitry], Kasturi, R.[Rangachar], Godavarthy, S.[Sridhar],
Detecting Wires in Cluttered Urban Scenes Using a Gaussian Model,
Not roads, powerlines, etc. BibRef

Gu, I.Y.H.[Irene Y.H.], Sistiaga, U.[Unai], Berlijn, S.M.[Sonja M.], Fahlström, A.[Anders],
Intelligent Video Surveillance for Detecting Snow and Ice Coverage on Electrical Insulators of Power Transmission Lines,
Springer DOI 0909

Gu, I.Y.H.[Irene Y.H.], Sistiaga, U.[Unai], Berlijn, S.M.[Sonja M.], Fahlstrom, A.[Anders],
Online detection of snow coverage and swing angles of electrical insulators on power transmission lines using videos,

Ituen, I., Sohn, G., Jenkins, A.,
A Case Study: Workflow Analysis of Power Line Systems for Risk Management,
ISPRS08(B3b: 331 ff).
PDF File. 0807

Hofler, H., Dambacher, M., Dimopoulos, N., Jetter, V.,
Monitoring and inspecting overhead wires and supporting structures,

Loehlein, O.[Otto],
Detection of Linear Objects for Synthetic Vision Applications,
SPIE(2736), 1996, pp. 128-135. Roads and power lines in radar images. BibRef 9600

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Transmission Towers, Pylons, Poles, Extraction, Radar, SAR, Lidar, Laser, Depth .

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