Open AccessKey Parts of Transmission Line Detection Using Improved YOLO v3
Author(s) -
Renwei Tu,
Zhongkui Zhu,
Bai Yongqiang,
Ming Gao,
Ge Zhang
Publication year - 2021
Publication title -
the international arab journal of information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 27
eISSN - 2309-4524
pISSN - 1683-3198
DOI - 10.34028/iajit/18/6/1
Subject(s) - computer science , artificial intelligence , key (lock) , cluster analysis , data set , electric power transmission , set (abstract data type) , transmission (telecommunications) , transmission line , line (geometry) , computer vision , pattern recognition (psychology) , real time computing , telecommunications , computer security , electrical engineering , engineering , programming language , geometry , mathematics
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%.