CODNet: a center and orientation detection network for power line following navigation

• Main Authors: Dai, Zhiyong, Yi, Jianjun, Zhang, Hanmo, Wang, Danwei, Huang, Xiaoci, Ma, Chao
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Navigation; Feature Extraction
• Online Access: https://hdl.handle.net/10356/163307
• Institution: Nanyang Technological University

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.