{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:51:22Z","timestamp":1776783082248,"version":"3.51.2"},"reference-count":116,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":89,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873267"],"award-info":[{"award-number":["61873267"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three\u2010dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR\u2010based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning\u2010based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.<\/jats:p>","DOI":"10.1155\/2021\/5559231","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T22:21:11Z","timestamp":1617229271000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Environment Perception Technologies for Power Transmission Line Inspection Robots"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2871-5812","authenticated-orcid":false,"given":"Minghao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yunong","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Shiyu","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Zhishuo","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4412-2953","authenticated-orcid":false,"given":"En","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zize","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Guo","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPETS.2015.2395388"},{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","unstructured":"ZhangC. LiuZ. YangS. andXuB. Key technologies of laser point cloud data processing in power line corridor Conference on LIDAR Imaging Detection and Target Recognition 2017 Changchun China.","DOI":"10.1117\/12.2295174"},{"key":"e_1_2_9_3_2","first-page":"98","article-title":"3D visualization technique of transmission line corridors: system design and implementation","volume":"48","author":"Mai X.","year":"2015","journal-title":"Electric Power"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-019-00230-w"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"GaoF. WangJ. KongZ. WuJ. FengN. WangS. HuP. LiZ. HuangH. andLiJ. Recognition of insulator explosion based on deep learning 14th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing 2017 Chengdu China 79\u201382.","DOI":"10.1109\/ICCWAMTIP.2017.8301453"},{"key":"e_1_2_9_6_2","first-page":"1","article-title":"Fittings detection in transmission line images with SSD model embedded occlusion relation module","volume":"15","author":"Zhao Z.","year":"2020","journal-title":"CAAI Transactions on Intelligent Systems"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20295"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"MontambaultS.andPouliotN. About the future of power line robotics 1st International Conference on Applied Robotics for the Power Industry 2010 Montreal QC Canada 1\u20136.","DOI":"10.1109\/CARPI.2010.5624466"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0924-2716(98)00009-4"},{"key":"e_1_2_9_10_2","first-page":"696","article-title":"Investigating laser scanner accuracy","volume":"34","author":"Boehler W.","year":"2003","journal-title":"The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth989"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.1330700"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"MayS. DroeschelD. HolzD. WiesenC. andFuchsS. 3d pose estimation and mapping with time-of-flight cameras Proceedings of IEEE\/RSJ International Conference on Intelligent Robots and Systems 2008 France.","DOI":"10.1109\/IROS.2009.5354684"},{"key":"e_1_2_9_14_2","first-page":"192","article-title":"Analysis of 3-d coordinate vision measuring methods with feature points on workpiece","volume":"8","author":"Zhu S.","year":"2000","journal-title":"Optics and Precision Engineering"},{"key":"e_1_2_9_15_2","first-page":"74","article-title":"Study on the measurement of transparent step by white-light interferometer","volume":"35","author":"Geng D.","year":"2013","journal-title":"Optical Instruments"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11340-010-9405-8"},{"key":"e_1_2_9_17_2","first-page":"20","article-title":"Transient 3D deformation measurement method by color splitting based on phase shift and ESPI","volume":"38","author":"Sun L.","year":"2016","journal-title":"Optical Instruments"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34141-0_16"},{"key":"e_1_2_9_19_2","first-page":"657","article-title":"Application of deep learning to 3d object reconstruction from a single image","volume":"45","author":"Chen J.","year":"2019","journal-title":"Acta Automatica Sinica"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2321373"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2493781"},{"key":"e_1_2_9_22_2","unstructured":"StewartC.andDyerC. The trinocular general support algorithm: a three-camera stereo algorithm for overcoming binocular matching errors 1988 Second International Conference on Computer Vision 1988 Los Alamitos USA 134\u2013138."},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"GoeseleM. SnavelyN. CurlessB. HoppeH. andSeitzS. M. Multi-view stereo for community photo collections 2007 IEEE 11th International Conference on Computer Vision 2007 Rio de Janeiro Brazil 1\u20138.","DOI":"10.1109\/ICCV.2007.4408933"},{"key":"e_1_2_9_24_2","first-page":"3","article-title":"Digital terrain model","volume":"3","author":"Yang D.","year":"1998","journal-title":"Bulletin of Surveying and Mapping"},{"key":"e_1_2_9_25_2","first-page":"110","article-title":"Dem generation from laser scanner data using adaptive tin models","volume":"33","author":"Axelsson P.","year":"2000","journal-title":"International Archives of Photogrammetry and Remote Sensing"},{"key":"e_1_2_9_26_2","first-page":"1275","article-title":"Powerlines extraction techniques from airborne LiDAR data","volume":"36","author":"Yu J.","year":"2011","journal-title":"Geomatics and Information Science of Wuhan University"},{"key":"e_1_2_9_27_2","first-page":"935","article-title":"Slope based filtering of laser altimetry data","volume":"33","author":"Vosselman G.","year":"2000","journal-title":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2003.810682"},{"key":"e_1_2_9_29_2","first-page":"61","article-title":"Power lines extraction from airborne LiDAR data using spatial domain segmentation","volume":"18","author":"Liu Z.","year":"2014","journal-title":"Journal of Remote Sensing"},{"key":"e_1_2_9_30_2","first-page":"982","article-title":"An automatic power line extraction method from airborne light detection and ranging point cloud in complex terrain","volume":"46","author":"Shen X.","year":"2018","journal-title":"Journal of Tongji University. Natural Science"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2005.863390"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.14358\/PERS.78.11.1227"},{"key":"e_1_2_9_33_2","first-page":"500","article-title":"Power line extraction and reconstruction from airborne LiDAR point cloud","volume":"43","author":"Wu J.","year":"2019","journal-title":"Laser Technology"},{"key":"e_1_2_9_34_2","unstructured":"MelzerT.andBrieseC. Extraction and modeling of power lines from ALS abstract: point clouds 28th Workshop of Austrian Association for Pattern Recognition 2004 47\u201354."},{"key":"e_1_2_9_35_2","first-page":"109","article-title":"A method of reconstructing 3d powerlines from airborne LiDAR point clouds","volume":"41","author":"Lin X.","year":"2016","journal-title":"Science of Surveying and Mapping"},{"key":"e_1_2_9_36_2","first-page":"1223","article-title":"Powerline three-dimensional reconstruction for LiDAR point cloud data","volume":"18","author":"Lai X.","year":"2014","journal-title":"Journal of Remote Sensing"},{"key":"e_1_2_9_37_2","first-page":"347","article-title":"3d power line reconstruction from airborne LiDAR point cloud of overhead electric power transmission corridors","volume":"45","author":"Lin X.","year":"2016","journal-title":"Acta Geodetica et Cartographica Sinica"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs6043302"},{"key":"e_1_2_9_39_2","doi-asserted-by":"crossref","unstructured":"LiangJ. ZhangJ. DengK. LiuZ. andShiQ. A new power-line extraction method based on airborne LiDAR point cloud data 2011 International Symposium on Image and Data Fusion 2011 Tengchong China 1\u20134.","DOI":"10.1109\/ISIDF.2011.6024293"},{"key":"e_1_2_9_40_2","first-page":"1565","article-title":"Comparison and analysis of models for 3d power line reconstruction using LiDAR point cloud","volume":"42","author":"Zhang J.","year":"2017","journal-title":"Geomatics and Information Science of Wuhan University"},{"key":"e_1_2_9_41_2","first-page":"10","article-title":"Automatic 3d power line reconstruction of multi-angular imaging power line inspection system","volume":"6752","author":"Zhang W.","year":"2007","journal-title":"Proceedings of SPIE - The International Society for Optical Engineering"},{"key":"e_1_2_9_42_2","doi-asserted-by":"crossref","unstructured":"GanovelliF. MalomoL. andScopignoR. Reconstructing power lines from images 2018 International Conference on Image and Vision Computing New Zealand 2018 Auckland New Zealand 1\u20136.","DOI":"10.1109\/IVCNZ.2018.8634765"},{"key":"e_1_2_9_43_2","doi-asserted-by":"crossref","unstructured":"MaurerM. HoferM. FraundorferF. andBischofH. Automated inspection of power line corridors to measure vegetation undercut using UAV-based images International Conference on Unmanned Aerial Vehicles in Geomatics 2017 Bonn Germany 33\u201340.","DOI":"10.5194\/isprs-annals-IV-2-W3-33-2017"},{"key":"e_1_2_9_44_2","doi-asserted-by":"crossref","unstructured":"ChenZ. LanZ. LongH. andHuQ. 3d modeling of pylon from airborne lidar data 2014 9158 Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China International Society for Optics and Photonics 915807.","DOI":"10.1117\/12.2063873"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs8030243"},{"key":"e_1_2_9_46_2","first-page":"89","article-title":"The design of unmanned aerial vehicle power patrol base on oblique photography technology","volume":"32","author":"Xi L.","year":"2019","journal-title":"Electronic Science and Technology"},{"key":"e_1_2_9_47_2","first-page":"292","article-title":"3d reconstruction of transmission route based on UAV oblique photogrammetry","volume":"41","author":"Pei H.","year":"2016","journal-title":"Science of Surveying and Mapping"},{"key":"e_1_2_9_48_2","unstructured":"ZouZ. ShiZ. GuoY. andYeJ. Object detection in 20 years: a survey 2019."},{"key":"e_1_2_9_49_2","doi-asserted-by":"crossref","unstructured":"GirshickR. DonahueJ. DarrellT. andMalikJ. Rich feature hierarchies for accurate object detection and semantic segmentation 2014 IEEE Conference on Computer Vision and Pattern Recognition 2014 Columbus OH 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_2_9_50_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_2_9_51_2","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_9_52_2","doi-asserted-by":"crossref","unstructured":"NeubeckA.andVan GoolL. Efficient non-maximum suppression 3 18th International Conference on Pattern Recognition 2006 Hong Kong China 850\u2013855.","DOI":"10.1109\/ICPR.2006.479"},{"key":"e_1_2_9_53_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_2_9_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2007.09.014"},{"key":"e_1_2_9_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"e_1_2_9_56_2","doi-asserted-by":"crossref","unstructured":"DalalN.andTriggsB. Histograms of oriented gradients for human detection 1 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 San Diego CA 886\u2013893.","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_2_9_57_2","doi-asserted-by":"crossref","unstructured":"SampedroC. MartinezC. ChauhanA. andCampoyP. A supervised approach to electric tower detection and classification for power line inspection 2014 International Joint Conference on Neural Networks 2014 Beijing China 1970\u20131977.","DOI":"10.1109\/IJCNN.2014.6889836"},{"key":"e_1_2_9_58_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2015.0477"},{"key":"e_1_2_9_59_2","first-page":"12","article-title":"A supervised approach to electric tower detection for power line inspection","volume":"38","author":"Wang X.","year":"2017","journal-title":"Northeast Electric Power Technology"},{"key":"e_1_2_9_60_2","article-title":"Method for orientation determination of transmission line tower based on visual navigation","volume":"56","author":"Wang Z.","year":"2019","journal-title":"Laser and Optoelectronics Progress"},{"key":"e_1_2_9_61_2","first-page":"104","article-title":"Coarse-to-fine detection for nests on pylon","volume":"3","author":"Zhang Y.","year":"2017","journal-title":"Information Technology"},{"key":"e_1_2_9_62_2","first-page":"231","article-title":"Method for detecting bird\u2032s nest on tower based on UAV image","volume":"53","author":"Xu J.","year":"2017","journal-title":"Computer Engineering and Application"},{"key":"e_1_2_9_63_2","unstructured":"ZhaoZ. LiuN. andYuanY. The recognition and localization of insulators based on SIFT and RANSAC Proceedings of 3rd International Conference on Multimedia Technology 2013 Guangzhou China 692\u2013699."},{"key":"e_1_2_9_64_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2014.06.094"},{"key":"e_1_2_9_65_2","doi-asserted-by":"crossref","unstructured":"PrasadP. S.andRaoB. P. LBP-HF features and machine learning applied for automated monitoring of insulators for overhead power distribution lines 2016 International Conference on Wireless Communications Signal Processing and Networking 2016 Chennai India 808\u2013812.","DOI":"10.1109\/WiSPNET.2016.7566245"},{"key":"e_1_2_9_66_2","first-page":"254","article-title":"Detection for transmission line obstacles based on principal component gradient histogram","volume":"52","author":"Zhang F.","year":"2016","journal-title":"Computer Engineering and Application"},{"key":"e_1_2_9_67_2","first-page":"147","article-title":"An vibration damper detection algorithm combined with aggregation channel and complex frequency domain features","volume":"30","author":"Zhang D.","year":"2020","journal-title":"Computer Technology and Development"},{"key":"e_1_2_9_68_2","first-page":"135","article-title":"A bolt detection method for pictures captured from an unmanned aerial vehicle in power transmission line inspection","volume":"33","author":"Feng M.","year":"2018","journal-title":"Journal of Electric Power Science and Technology"},{"key":"e_1_2_9_69_2","first-page":"1514","article-title":"Vision-based tracing, recognition and positioning strategy for bolt tightening live working robot on power transmission line","volume":"31","author":"Fan S.","year":"2017","journal-title":"Journal of Electronic Measurement and Instrumentation"},{"key":"e_1_2_9_70_2","first-page":"11","article-title":"Research overview on visual detection of transmission lines based on deep learning","volume":"32","author":"Zhao Z.","year":"2019","journal-title":"Guangdong Electric Power"},{"key":"e_1_2_9_71_2","doi-asserted-by":"crossref","unstructured":"GirshickR. Fast R-CNN 2015 IEEE International Conference on Computer Vision 2015 Santiago Chile 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_2_9_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_2_9_73_2","doi-asserted-by":"crossref","unstructured":"RedmonJ. DivvalaS. GirshickR. andFarhadiA. You only look once: unified real-time object detection 2016 IEEE Conference on Computer Vision and Pattern Recognition 2016 Seattle WA USA 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_9_74_2","doi-asserted-by":"crossref","unstructured":"RedmonJ.andFarhadiA. YOLO9000: better faster stronger 2017 IEEE Conference on Computer Vision and Pattern Recognition 2017 Honolulu HI USA 6517\u20136525.","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_2_9_75_2","unstructured":"RedmonJ.andFarhadiA. YOLOv3: an incremental improvement 2018."},{"key":"e_1_2_9_76_2","unstructured":"BochkovskiyA. WangC. Y. andLiaoH. Y. YOLOv4: optimal speed and accuracy of object detection 2020."},{"key":"e_1_2_9_77_2","first-page":"17","article-title":"YOLO-based real-time detection of power line poles from unmanned aerial vehicle inspection vision","volume":"52","author":"Guo J.","year":"2019","journal-title":"Electric Power"},{"key":"e_1_2_9_78_2","doi-asserted-by":"crossref","unstructured":"WangH. YangG. LiE. TianY. ZhaoM. andLiangZ. High-voltage power transmission tower detection based on Faster R-CNN and YOLO-V3 2019 Chinese Control Conference 2019 Guangzhou China 8750\u20138755.","DOI":"10.23919\/ChiCC.2019.8866322"},{"key":"e_1_2_9_79_2","first-page":"53","article-title":"Intelligent detection of bird\u2032s nest based on RetinaNet model","volume":"23","author":"Shi L.","year":"2020","journal-title":"Power Systems and Big Data"},{"key":"e_1_2_9_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"e_1_2_9_81_2","first-page":"37","article-title":"Bird\u2032s nest detection in multi-scale of high-voltage tower based on Faster R-CNN","volume":"43","author":"Wang J.","year":"2019","journal-title":"Journal of Beijing Jiaotong University"},{"key":"e_1_2_9_82_2","doi-asserted-by":"crossref","unstructured":"LiuX. JiangH. ChenJ. ChenJ. ZhuangS. andMiaoX. Insulator detection in aerial images based on faster regions with convolutional neural network 14th International Conference on Control and Automation 2018 Anchorage AK USA 1082\u20131086.","DOI":"10.1109\/ICCA.2018.8444172"},{"key":"e_1_2_9_83_2","article-title":"Insulator detection method in inspection image based on improved Faster R-CNN","volume":"12","author":"Zhao Z.","year":"2019","journal-title":"Energies"},{"key":"e_1_2_9_84_2","article-title":"A method of insulator faults detection in aerial images for high-voltage transmission lines inspection","volume":"9","author":"Han J.","year":"2019","journal-title":"Applied Sciences"},{"key":"e_1_2_9_85_2","first-page":"213","article-title":"Multi-target detection and location of transmission line inspection image based on improved faster-RCNN","volume":"39","author":"Lin G.","year":"2019","journal-title":"Electric Power Automation Equipment"},{"key":"e_1_2_9_86_2","first-page":"173","article-title":"Real-time detection of power transmission line key components based on YOLOv3","volume":"42","author":"Dong S.","year":"2019","journal-title":"Electronic Measurement Technology"},{"key":"e_1_2_9_87_2","first-page":"54","article-title":"Detection of key components of transmission lines based on multi-scale feature fusion","volume":"57","author":"Yang G.","year":"2020","journal-title":"Electric Measurement and Instrumentation"},{"key":"e_1_2_9_88_2","first-page":"212","article-title":"Comparative study of transmission line component detection models based on UAV front end and SSD algorithm","volume":"51","author":"Yang G.","year":"2020","journal-title":"Journal of Taiyuan University of Technology"},{"key":"e_1_2_9_89_2","doi-asserted-by":"crossref","unstructured":"LiuW. AnguelovD. ErhanD. SzegedyC. ReedS. FuC. Y. andBergA. C. SSD: single shot multibox detector 14th European Conference on Computer Vision 2016 Cham 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_2_9_90_2","first-page":"7","article-title":"Fittings detection method in patrol images of transmission line based on improved SSD","volume":"56","author":"Qi Y.","year":"2019","journal-title":"Electric Measurement and Instrumentation"},{"key":"e_1_2_9_91_2","first-page":"162","article-title":"Object detection method for aerial inspection image based on region-based fully convolutional network","volume":"43","author":"Liu S.","year":"2019","journal-title":"Automation of Electric Power Systems"},{"key":"e_1_2_9_92_2","doi-asserted-by":"crossref","unstructured":"SinghB.andDavisL. S. An analysis of scale invariance in object detection - SNIP 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2018 Salt Lake City UT USA 3578\u20133587.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"e_1_2_9_93_2","doi-asserted-by":"crossref","unstructured":"LiY. ChenY. WangN. andZhangZ. Scale-aware trident networks for object detection 2019 IEEE\/CVF International Conference on Computer Vision 2019 Seoul South Korea 6053\u20136062.","DOI":"10.1109\/ICCV.2019.00615"},{"key":"e_1_2_9_94_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.105862"},{"key":"e_1_2_9_95_2","doi-asserted-by":"publisher","DOI":"10.1109\/70.538972"},{"key":"e_1_2_9_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/70.760345"},{"key":"e_1_2_9_97_2","doi-asserted-by":"crossref","unstructured":"ChaumetteF.andMalisE. 2-1\/2-D visual servoing: a possible solution to improve image-based and position-based visual servoings IEEE International Conference on Robotics and Automation 2000 San Francisco CA USA 630\u2013635.","DOI":"10.1109\/ROBOT.2000.844123"},{"key":"e_1_2_9_98_2","first-page":"849","article-title":"Survey of robot visual servoing","volume":"16","author":"Zhao Q.","year":"2001","journal-title":"Control and Decision"},{"key":"e_1_2_9_99_2","first-page":"767","article-title":"Survey on uncalibrated robot visual servoing control","volume":"48","author":"Tao B.","year":"2016","journal-title":"Chinese Journal of Theoretical and Applied Mechanics"},{"key":"e_1_2_9_100_2","first-page":"451","article-title":"Visual-servo-based line-grasping control for power transmission line inspection robot","volume":"29","author":"Wang L.","year":"2007","journal-title":"Robot"},{"key":"e_1_2_9_101_2","first-page":"111","article-title":"Visual servo control of obstacle negotiation for overhead power line inspection robot","volume":"29","author":"Zhang Y.","year":"2007","journal-title":"Robot"},{"key":"e_1_2_9_102_2","doi-asserted-by":"crossref","unstructured":"ZhaoD. YangG. LiE. andLiangZ. Design and its visual servoing control of an inspection robot for power transmission lines 2013 IEEE International Conference on Robotics and Biomimetics 2013 Shenzhen China 546\u2013551.","DOI":"10.1109\/ROBIO.2013.6739516"},{"key":"e_1_2_9_103_2","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1218.2012.00620"},{"key":"e_1_2_9_104_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11771-014-2173-3"},{"key":"e_1_2_9_105_2","doi-asserted-by":"crossref","unstructured":"HeT. WangH. ChenW. andWangW. Visual servoing of a new designed inspection robot for autonomous transmission line grasping International Conference on Wearable Sensors and Robots 2017 Singapore 553\u2013569.","DOI":"10.1007\/978-981-10-2404-7_42"},{"key":"e_1_2_9_106_2","first-page":"589","article-title":"Manipulator double close loop autonomous localization control of high-voltage cable mobile operation robot","volume":"39","author":"Jiang W.","year":"2019","journal-title":"Transactions of Beijing Institute of Technology"},{"key":"e_1_2_9_107_2","first-page":"123","article-title":"On auto-docking charging control method for the inspection robot","volume":"48","author":"Wu G.","year":"2016","journal-title":"Journal of Harbin Institute of Technology"},{"key":"e_1_2_9_108_2","doi-asserted-by":"crossref","unstructured":"AraarO.andAoufN. Visual servoing of a quadrotor UAV for autonomous power lines inspection 22nd Mediterranean Conference on Control and Automation 2014 Palermo Italy 1418\u20131424.","DOI":"10.1109\/MED.2014.6961575"},{"key":"e_1_2_9_109_2","unstructured":"XieH. LynchA. andJagersandM. IBVS of a rotary wing UAV using line features IEEE 27th Canadian Conference on Electrical and Computer Engineering 2014 Toronto Canada 1\u20136."},{"key":"e_1_2_9_110_2","doi-asserted-by":"crossref","unstructured":"MillsS. AoufN. andMejiasL. Image based visual servo control for fixed wing UAVs tracking linear infrastructure in wind 2013 IEEE International Conference on Robotics and Automation 2013 Karlsruhe Germany 5769\u20135774.","DOI":"10.1109\/ICRA.2013.6631406"},{"key":"e_1_2_9_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2020.2967851"},{"key":"e_1_2_9_112_2","doi-asserted-by":"crossref","unstructured":"BilenH.andVedaldiA. Weakly supervised deep detection networks 2016 IEEE Conference on Computer Vision and Pattern Recognition 2016 Seattle WA 2846\u20132854.","DOI":"10.1109\/CVPR.2016.311"},{"key":"e_1_2_9_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2876304"},{"key":"e_1_2_9_114_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103232"},{"key":"e_1_2_9_115_2","unstructured":"MontambaultS.andPouliotN. Design and validation of a mobile robot for power line inspection and maintenance 6th International Conference on Field and Service Robotics 2007 Chamonix France."},{"key":"e_1_2_9_116_2","doi-asserted-by":"crossref","unstructured":"RichardP. L. PouliotN. MorinF. LepageM. HamelinP. LagacM. SartorA. LambertG. andMontambaultS. LineRanger: analysis and field testing of an innovative robot for efficient assessment of bundled high-voltage powerlines 2019 International Conference on Robotics and Automation 2019 Montreal QC Canada 9130\u20139136.","DOI":"10.1109\/ICRA.2019.8794397"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/5559231.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/5559231.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5559231","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T07:54:20Z","timestamp":1724745260000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5559231"}},"subtitle":[],"editor":[{"given":"Bin","family":"Gao","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":116,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5559231"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5559231","archive":["Portico"],"relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"value":"1687-725X","type":"print"},{"value":"1687-7268","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-01-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5559231"}}