TLI-YOLOv5: A Lightweight Object Detection Framework for Transmission Line Inspection by Unmanned Aerial Vehicle

Abstract

Unmanned aerial vehicles (UAVs) have become an important tool for transmission line inspection, and the inspection images taken by UAVs often contain complex backgrounds and many types of targets, which poses many challenges to object detection algorithms. In this paper, we propose a lightweight object detection framework, TLI-YOLOv5, for transmission line inspection tasks. Firstly, we incorporate the parameter-free attention module SimAM into the YOLOv5 network. This integration enhances the network’s feature extraction capabilities, without introducing additional parameters. Secondly, we introduce the Wise-IoU (WIoU) loss function to evaluate the quality of anchor boxes and allocate various gradient gains to them, aiming to improve network performance and generalization capabilities. Furthermore, we employ transfer learning and cosine learning rate decay to further enhance the model’s performance. The experimental evaluations performed on our UAV transmission line inspection dataset reveal that, in comparison to the original YOLOv5n, TLI-YOLOv5 increases precision by 0.40%, recall by 4.01%, F1 score by 1.69%, mean average precision at 50% IoU (mAP50) by 2.91%, and mean average precision from 50% to 95% IoU (mAP50-95) by 0.74%, while maintaining a recognition speed of 76.1 frames per second and model size of only 4.15 MB, exhibiting attributes such as small size, high speed, and ease of deployment. With these advantages, TLI-YOLOv5 proves more adept at meeting the requirements of modern, large-scale transmission line inspection operations, providing a reliable, efficient solution for such demanding tasks.