Edge-Aware Transformer Encoder for UAV-Based Power Grid Defect Detection

UAV-based inspection is the dominant approach for power grid defect detection, yet accurate identification of small-texture insulator discharge traces and structural line clamp insulation cover missing defects remains challenging. The state-of-the-art RT-DETR suffers from unsatisfactory fine-grained detection performance, as its AIFI encoder’s standard attention is insensitive to edge information and the feed-forward network lacks spatial inductive bias. To address this, we propose EA-AIFI, an edge-aware Transformer encoder module. It introduces a dual-scale differential edge-aware attention bias to focus on defect boundaries, and a spatial-aware ConvFFN to capture subtle texture and structural anomalies. Experiments on our private dataset show that EA-AIFI achieves 4.5% absolute mAP@0.5 improvement over baseline RT-DETR-R18 (0.609 vs. 0.564), with a remarkable 7.8% boost for insulator discharge traces, while maintaining real-time inference. With negligible overhead, it is highly suitable for edge deployment in industrial power inspection.