The application of a lightweight model FA-YOLOv5 with fused attention mechanism in insulator defect detection

Abstract

Insulators are important components of transmission lines, serving as support for conductors and preventing current backflow. However, insulators exposed to natural environments for a long time are prone to failure and can cause huge economic losses. This article proposes a fast and accurate lightweight Fast and Accurate YOLOv5s (FA-YOLO) model based on YOLOv5s model. Firstly, attention mechanisms are integrated into the network module, improving the model’s ability to extract and fuse target features. Secondly, the backbone part of the network is lightweightened to reduce the number of parameters and computations at the cost of slightly reducing the accuracy of detecting a few objects. Finally, the loss function of the model is improved to accelerate the convergence of the network and improve detection accuracy. At the same time, a visual insulator detection interface is designed using PyQt5. The experimental results show that the algorithm in this paper reduces the number of parameters by 28.6%, the computational effort by 35.7%, and the mAP value by 1.7% compared with the original algorithm, and is able to identify defective insulators quickly and accurately in complex backgrounds.