E3S Web Conf.
Volume 300, 20212021 2nd International Conference on Energy, Power and Environmental System Engineering (ICEPESE2021)
|Number of page(s)||6|
|Section||Energy and Power Engineering|
|Published online||06 August 2021|
Detection method based on improved faster R-CNN for pin defect in transmission lines
Maintenance Company, State Grid Hubei Electric Power Co., Ltd., Wuhan, China
2 School of Electrical Engineering and Automation, Wuhan University, Wuhan, China
3 Guangzhou China Sciences Intelligent Inspection Technology Co., Ltd, Guangzhou, China
* Corresponding author: email@example.com
Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%
© The Authors, published by EDP Sciences, 2021
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