A fault diagnosis model for insulator instance segmentation in transmission lines based on ELD-YOLOv8

Abstract A novel ELD-YOLOv8n transmission line insulator instance segmentation fault diagnosis model is proposed to accurately and meticulously segment every fault of the transmission line insulator. Firstly, an innovative and efficient lightweight downsampling module (ELD) was proposed, Efficient-Lightweight downsampling, This module is used to replace the standard downsampling unit in the model, which not only reduces the number of model parameters, but also enhances the feature extraction ability of the model. Then, a lightweight CARAFE module was used to replace the upsampling of the model, optimizing the upsampling process and reducing the number of parameters. Finally, CGAFusion is used to fuse the features extracted from the backbone network with the head features, effectively compensating for the information loss caused by the convolution process. The experimental results show that the improved model proposed in this study achieved an mAP@50 of 86.2%. The effectiveness of the improvement and significant instance segmentation fault detection capability have been demonstrated through ablation experiments. This study provides a new technical path for fault diagnosis of insulator instance segmentation in transmission lines.