Lightweight improved transmission line insulator defect detection algorithm of YOLOv8

Abstract

The use of drones for insulator defect inspection of transmission lines has become the mainstream in the industry. In response to the problems of low detection speed, insufficient detection accuracy, high network complexity, and difficulty in deploying to mobile devices such as drones for insulator defects in power lines, propose a lightweight improved power transmission lines insulator defects algorithm of YOLOv8. Firstly, replace the backbone network of YOLOv8 with a lightweight MobileNetv3 network to reduce the number of parameters. Efficient Multi-Scale Attention (EMA) is used in MobileNetv3 to more accurately locate and identify objects. Secondly, Ghost Shuffle convolution (GSConv) is introduced to redesign the feature fusion network to ensure detection accuracy while reducing computing consumption. Finally, using the MPDIoU loss function to improve training convergence speed and make prediction box results more accurate. Experimental results can prove that the lightweight improved model achieves an accuracy of 97. 1% and a recall rate of 97. 3%, with a 70% reduction in the number of model parameters. It is more suitable for deployment on drone platforms and meets the requirements real-time and accuracy requirements of insulator defects detection on the edge side of transmission lines.