Robust Transmission Line Defect Detection in Fog via Structure-Preserving and Degradation-Aware Enhancement

Unmanned aerial vehicle (UAV)-based inspection is essential for transmission line maintenance, where object detection enables reliable identification of component states and defects. However, fog-induced degradation reduces image contrast and suppresses fine structural cues, thereby significantly degrading detection performance. To address this issue, we propose a robust detection framework, termed FogTLD-YOLO, for defect detection under foggy conditions. The proposed model adopts a degradation-adaptive enhancement strategy to mitigate feature deterioration. A fog-aware gated compensation module leverages frequency-domain priors to selectively compensate degraded regions, while a structural-positional enhancement pyramid preserves geometric continuity and positional sensitivity during feature aggregation. Together, these designs improve the representation of slender structures and small rgb]1,0,0objects. Extensive experiments show that FogTLD-YOLO achieves 82.1% mAP50, outperforming the best competitive algorithm by 2.8% with comparable efficiency. Comprehensive analyses, including module insertion strategies, gating design variants, convolutional branch configurations, and cross-architecture evaluations, further validate the effectiveness and general applicability of the proposed design for robust defect detection in foggy inspection scenarios.