Key Parts of Transmission Line Detection Using Improved YOLO v3
Tu Renwei1, Zhu Zhongjie1, Bai Yongqiang1, Gao Ming2, and Ge Zhifeng2
1College of Information and Intelligence Engineering, Zhejiang Wanli University, China
2Ninghai Power Supply Company Limited, State Grid Corporation of Zhejiang, China
Abstract: Unmanned Aerial Vehicle (UAV) inspection has become one of main methods for current transmission line inspection, but there are still some shortcomings such as slow detection speed, low efficiency, and inability for low light environment. To address these issues, this paper proposes a deep learning detection model based on You Only Look Once (YOLO) v3. On the one hand, the neural network structure is simplified, that is the three feature maps of YOLO v3 are pruned into two to meet specific detection requirements. Meanwhile, the K-means++ clustering method is used to calculate the anchor value of the data set to improve the detection accuracy. On the other hand, 1000 sets of power tower and insulator data sets are collected, which are inverted and scaled to expand the data set, and are fully optimized by adding different illumination and viewing angles. The experimental results show that this model using improved YOLO v3 can effectively improve the detection accuracy by 6.0%, flops by 8.4%, and the detection speed by about 6.0%.
Keywords: Deep learning, YOLO v3, electric tower, insulator.
Received October 31, 2019; accepted February 4, 2021