Research on Defect Detection Method of Railway Transmission Line Insulators Based on GC-YOLO

Abstract

The insulator defect targets in Unmanned Aerial Vehicle (UAV) images are often small and set against complex backgrounds. Consequently, traditional object detection algorithms commonly struggle to identify these minor defects. To enhance precision and recall in detecting insulator defects, a novel model, GC-YOLO (ghost convolution and centralized feature pyramid -You Only Look Once), based on YOLOv5s, has been introduced. GC-YOLO incorporates the Ghost convolution module in the backbone network, reducing feature redundancy and improving the inference speed of the feature extraction network. Moreover, an attention mechanism based on Coordinate Attention (CA) is integrated at the terminal of the backbone network, aimed at emphasizing the extraction of crucial information from target features. The Explicit Visual Center Block (EVCBlock) module from Centralized Feature Pyramid Network (CFPNet) is introduced in the neck layer to effectively fuse multi-scale features and enhance the feature map’s characterization capability. Furthermore, in order to enhance the precision in detecting small-sized defects, a small object detection head is also added to the detection layer based on CFPNet. Experimental results demonstrate that GC-YOLO achieves a recall of 89.7% and [email protected] of 94.2%, surpassing YOLOv5s by 7% and 6.5%, respectively. The proposed algorithm exhibits superior detection precision in complex scenes, providing a theoretical basis for intelligent and mechanized railway monitoring systems.

Keywords

• GC-YOLO
• insulator-defect detection
• CFPNet
• EVCBlock
• ghost convolution