文章基本信息

标题：Key Parts of Transmission Line Detection Using Improved YOLO v3
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作者：Tu Renwei ; Zhu Zhongjie ; Bai Yongqiang 等
期刊名称：The International Arab Journal of Information Technology
印刷版ISSN：1683-3198
出版年度：2021
卷号：18
期号：6
DOI：10.34028/iajit/18/6/1
语种：English
出版社：Zarqa Private University
摘要：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%.
关键词：Deep learning;YOLO v3;electric tower;insulator