Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line

Abstract

Abstract Combining edge devices with intelligent inspection for transmission lines can fulfill the demand for real‐time defect detection in the field. However, there has been limited research on algorithms suitable for edge devices with low computational power and memory, and the existing research primarily focuses on CPU optimization. To address these issues, this paper proposes a real‐time defect detection method for transmission line endpoints based on YOLO‐GSS (YOLOv8 with Mosaic‐9, G‐GhostNet, S‐FPN, and Spatial Intersection over Union (SIoU) modifications). First, the authors improve the input of the YOLOv8 network using Mosaic‐9 to increase the number of input features in the training phase and enhance algorithm robustness. Next, the authors introduce G‐GhostNet and S‐FPN to enhance the backbone and neck sections while improving inference speed and accuracy. Finally, the authors modify the Complete Intersection over Union loss function of YOLOv8 using SIoU to further improve the detection accuracy. Experimental results demonstrate that compared to the original YOLOv8, the proposed method achieves a 5x increase in inference speed on Nvidia Jetson NX edge devices and a 7.7% improvement in accuracy, meeting the real‐time defect detection requirements for transmission line field inspections.

Keywords

• edge detection
• power cables