Open AccessPin Defect Detection Method of UAV Patrol Overhead Line Based on Cascaded Convolution Network
Author(s) -
Chaoyue Gu,
Zhe Li,
Jianxin Shi,
Gehao Sheng,
Xiuchen Jiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1659/1/012021
Subject(s) - fastener , overhead (engineering) , computer science , convolution (computer science) , line (geometry) , generalization , transmission line , artificial intelligence , transmission (telecommunications) , power (physics) , image (mathematics) , electric power transmission , computer vision , real time computing , embedded system , engineering , artificial neural network , electrical engineering , mathematics , telecommunications , structural engineering , mathematical analysis , physics , geometry , quantum mechanics , operating system
Pin plays the role of fixing power equipment on the overhead line. Once it is missing, it will lay a serious hidden danger for the normal operation of the overhead line. In order to improve the efficiency of UAV patrol transmission line and improve the detection rate of pin defect of transmission line, this paper proposes a pin defect detection method based on cascaded convolution network. In view of the complex background of inspection image and the small size of pin, the overall detection method is divided into two parts: positioning and diagnosis. Firstly, all fastener positions including pins are located by the improved Faster-RCNN network, and then RetinaNet network is cascaded to diagnose the defects of the fastener. The difficulty of learning is decreased and the generalization ability is improved in this way. Finally the experiment shows that this method can effectively detect the pin defects in the UAV patrol image.