{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T07:04:10Z","timestamp":1775891050478,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001156"],"award-info":[{"award-number":["62001156"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BE2019036"],"award-info":[{"award-number":["BE2019036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BE2020092"],"award-info":[{"award-number":["BE2020092"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Plan of Jiangsu Province","award":["62001156"],"award-info":[{"award-number":["62001156"]}]},{"name":"Key Research and Development Plan of Jiangsu Province","award":["BE2019036"],"award-info":[{"award-number":["BE2019036"]}]},{"name":"Key Research and Development Plan of Jiangsu Province","award":["BE2020092"],"award-info":[{"award-number":["BE2020092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance.<\/jats:p>","DOI":"10.3390\/rs14164095","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Dual-View Stereovision-Guided Automatic Inspection System for Overhead Transmission Line Corridor"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3788-8122","authenticated-orcid":false,"given":"Yaqin","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou 213022, China"},{"name":"Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5968-9290","authenticated-orcid":false,"given":"Chang","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou 213022, China"}]},{"given":"Yunfeng","family":"Dai","sequence":"additional","affiliation":[{"name":"State Grid Yancheng Power Supply Company, Yancheng 224000, China"}]},{"given":"Xingming","family":"Feng","sequence":"additional","affiliation":[{"name":"State Grid Yancheng Power Supply Company, Yancheng 224000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6077-3097","authenticated-orcid":false,"given":"Yunpeng","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou 213022, China"},{"name":"Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3224-9831","authenticated-orcid":false,"given":"Qingwu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou 213022, China"},{"name":"Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1109\/TSMC.2018.2871750","article-title":"Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks","volume":"50","author":"Tao","year":"2020","journal-title":"IEEE Trans. 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