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

Abstract

The insulator defect targets in UAV (Unmanned Aerial Vehicle) 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

Insulator Defect Detection Based on ML-YOLOv5 Algorithm

“…We integrated the CA attention mechanism into the backbone module and introduced Bi-FPN [ 39 ] in the neck module to replace the original PANet, thereby enhancing the network’s capability for detecting small objects. Ding et al [ 40 ] proposed a novel model, GC-YOLO, based on the improvement of YOLOv5s. GC-YOLO integrates the Ghost convolution module into the backbone network, adds the CA attention mechanism, introduces the EVCBlock module in the neck layer, and includes an additional small object detection head in the detection layer.…”

SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

“…Di et al [19] introduced multilayer convolutional operations and feature pyramid structure into YOLOv5 and established a target detection model suitable for transmission lines. Lu et al [20], based on the YOLOv5s algorithm, incorporated a lightweight Ghost convolution module into the backbone network, which reduces the feature map redundancy and improves the inference speed in the feature extraction part of the model. In addition, an attention mechanism based on coordinated attention (CA) was incorporated to effectively extract key feature information.…”