Research on infrared insulator image recognition based on improved YOLOv5
Chaoyue Lang, Xiu Hua Ji, Beimin Xie, Hexin Wang, Peng Wu · 2023
As the scale of transmission lines is expanding, the safety of transmission line operation is getting more and more attention; insulators as an important component of transmission lines, the use of insulators greatly affects the safe operation of transmission lines. The traditional depth recognition algorithm can not achieve effective recognition of infrared images, in order to achieve rapid recognition of insulators in complex environments, this paper proposes the recognition of infrared insulator images based on the improved YOLOv5 depth neural network detection algorithm. Firstly, Ghost convolution was introduced into the backbone network to speed up detection and network lightweighting; secondly, to enhance the multi-scale convergence of networks, improved GAM attention module added behind the neck network; in addition, the network introduces an EIOU loss function for convergence; finally, validation of this improved algorithm with the collected infrared insulator dataset. The results show that the improved algorithm in this paper achieves 91.2% accuracy and 92.1% mAP on the infrared insulator dataset, which improves 2.3% and 4.1% compared with the test results of YOLOv5 model, simultaneous detection speed up to 91 FPS, which meets the real-time requirement.