YOLO-TDH: An object detection framework for power transmission line inspection

Abstract

Coupled with vision-based inspection techniques, unmanned aerial vehicles (UAVs) have been extensively applied to power transmission line inspection. UAV images typically cover a wide field of view, they often contain complex backgrounds in power transmission line inspection, which make the accurate detection and localization of small-size defects challenging, especially on resource-constrained devices. To tackle this issue, this paper proposes YOLO-TDH, a novel lightweight object detection framework derived from YOLOv8. The proposed YOLO-TDH incorporates three key components to improve the detection of small-size defects: (1) an Enhanced Feature Integration Module (EFIM), which strengthens multi-scale feature extraction and captures fine-grained details required to distinguish defective components from normal ones; (2) an Enhanced Feature Fusion Module (EFFM), which optimizes feature information flow and reduce the loss of critical details for small-size defects; and (3) a novel Transformer-based decoder, which models global context and alleviates ambiguity among overlapping components. The ShapeIoU loss is employed to improve the bounding box regression accuracy. Experiments on a dedicated transmission line dataset show that YOLO-TDH, Insulator dataset for targeted fault verification, and the public VisDrone2019 benchmark to validate generalization capability. The results demonstrate that YOLO-TDH consistently outperforms existing state-of-the-art lightweight methods in terms of Precision, Recall, mAP@0.5, and mAP@0.5:0.95. Results show that the proposed YOLO-TDH achieves a proper balance between diagnostic accuracy and computational efficiency, providing a robust solution for real-time, fine-grained health monitoring in resource-constrained scenarios.