文献类型：期刊文献

英文题名：CODNet: A Center and Orientation Detection Network for Power Line Following Navigation

作者：Dai, Zhiyong[1,2];Yi, Jianjun[1];Zhang, Hanmo[3,4];Wang, Danwei[5];Huang, Xiaoci[6];Ma, Chao[7]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]DeepBlue Acad Sci, Shanghai 200240, Peoples R China;[3]Shanghai Aerosp Control Technol Inst, Shanghai 201109, Peoples R China;[4]Shanghai Key Lab Aerosp Intelligent Control Techn, Shanghai 201109, Peoples R China;[5]Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore;[6]Shanghai Univ Engn Sci, Sch Mech & Automot Engn, Shanghai 201620, Peoples R China;[7]Henan Univ Sci & Technol, Sch Informat Engn, Luoyang 471023, Peoples R China

年份：2022

卷号：19

外文期刊名：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

收录：;EI(收录号：20220211454406);WOS:【SCI-EXPANDED(收录号:WOS:000732418700001)】；

基金：This work was supported in part by the Natural Science Fund of China (NSFC) under Grant 51575186; in part by the Shanghai Science and Technology Action Plan under Grant 18DZ1204000, Grant 18510745500, Grant 18510750100, and Grant 18510730600; and in part by the Shanghai Aerospace Science and Technology Innovation Fund (SAST) under Grant 2019-080 and Grant 2019-116.

语种：英文

外文关键词：Navigation; Feature extraction; Kernel; Inspection; Unmanned aerial vehicles; Heating systems; Image edge detection; Convolutional neural networks; power line detection

摘要：Recently, intelligent unmanned aerial vehicles (UAVs) have shown great advantages of flexibility and productivity in power line inspection, wherein robust detection of power lines from aerial images for automatic power line following navigation is required. However, identifying power lines accurately from a cluttered background is challenging due to the limited resolution of onboard cameras and the noisy environment. In this letter, we propose a novel power line detection method, denoted by CODNet, for the application of UAV navigation. Unlike existing works, the proposed method can extract features of power lines from cluttered backgrounds automatically and predict centers and orientations of power lines in the scene simultaneously. Besides, we introduce a new clustering method to summarize the average location and orientation of detected power lines as a guide for the automatic navigation of UAVs. Finally, experimental results demonstrate both the effectiveness and the superiority of the CODNet.