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Title: CODNet: a center and orientation detection network for power line following navigation
Authors: Dai, Zhiyong
Yi, Jianjun
Zhang, Hanmo
Wang, Danwei
Huang, Xiaoci
Ma, Chao
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Dai, Z., Yi, J., Zhang, H., Wang, D., Huang, X. & Ma, C. (2022). CODNet: a center and orientation detection network for power line following navigation. IEEE Geoscience and Remote Sensing Letters, 19, 8014805-.
Journal: IEEE Geoscience and Remote Sensing Letters
Abstract: 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.
ISSN: 1545-598X
DOI: 10.1109/LGRS.2021.3092399
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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