PGE-YOLO: A Multi-Fault-Detection Method for Transmission Lines Based on Cross-Scale Feature Fusion

Abstract

Addressing the issue of incorrect and missed detections caused by the complex types, uneven scales, and small sizes of defect targets in transmission lines, this paper proposes a defect-detection method based on cross-scale feature fusion, PGE-YOLO. Firstly, feature extraction is enriched by replacing the convolutional blocks in the backbone network that need to be cascaded and fused using the Par_C2f network module, which incorporates a parallel network (ParNet). Secondly, a four-layer efficient multi-scale attention (EMA) mechanism is incorporated into the network’s neck to address long and short dependency issues. This enhancement aims to improve global information retention by employing parallel substructures and integrating cross-space feature information. Finally, the paradigm of generalized feature fusion (GFPN) is introduced and reconfigured to develop a novel CE-GFPN. This model effectively integrates shallow feature information with deep feature information to enhance the capability of feature fusion and improve detection performance. Using a real transmission line multi-defect dataset from UAV aerial photography and the CPLID dataset, ablation and comparison experiments with various models demonstrated that our model achieved superior results. Compared to the initial YOLOv8n model, our model increased the detection accuracy by 6.6% and 1.2%, respectively, while ensuring there is no surge in the number of parameters. This ensures that the real-time and accuracy requirements for defect detection in the industry are satisfied.