Fault detection in power transmission lines using a KED-GNN approach

Abstract

Abstract The power system is a core infrastructure for the national economy and energy security. As key assets in the grid, transmission-line components must be accurately inspected from UAV images despite small targets, large scale variation, complex backgrounds, and partial image degradation. This paper proposes KED-GNN, a graph neural network enhanced by space-to-depth convolution, Kolmogorov–Arnold Network-based nonlinear transformation, and efficient multi-scale attention, for UAV-based transmission-line component and fault detection. Graph-based image representation methods, such as Vision GNN, provide a way to represent an image as a graph by treating image patches as nodes and their relationships as edges, which has potential for UAV transmission-line inspection. However, direct use of this representation still faces small-target information loss, multi-scale feature variation, and complex background interference. Therefore, KED-GNN introduces an SPD-Conv-based detail-preserving sampling algorithm, a KAN-based nonlinear feature transformation mechanism, and an EMA-based efficient multi-scale attention mechanism. Under the same Faster R-CNN detection framework, KED-GNN achieves a final mAP @ 0.5 of 0.86 and precision of 0.85, improving mAP @ 0.5 by 3.61%, 8.86%, and 24.64% relative to Vision GNN, ViTDet, and ResNet152, respectively. These results indicate that the proposed method can improve transmission-line component detection while maintaining a compact and practically deployable model design.