Pin Defect Detection in Power Transmission Lines via AAV-Based Automated Inspection and a Lightweight SADL-YOLOv11 Model for Edge Deployment

Unmanned aerial vehicles (UAVs) offer a safe and economical alternative to manual patrols of overhead transmission lines, yet reliable localisation of missing cotter pins remains difficult because the targets are pixel–scale and often masked by cluttered towers. This work presents SADL–YOLOv11, a lightweight single-stage detector embedded in an end-to-end UAV workflow. A visibility-aware waypoint optimiser first plans 3-D trajectories that maximise pin exposure while satisfying safety envelopes reconstructed from LiDAR point clouds. The captured images are analysed on board by the proposed network, which extends a YOLOv11 baseline with a slim-neck (GSConv, VoVGSCSP, DySample), a micro-scale P2 head, an Adaptive Fine-Grained Channel Attention module, a Normalized-Wasserstein Distance loss for sub-pixel box regression, and a detail-enhanced weight-shared head. Group-Taylor pruning and feature–logit distillation further compress the model for edge deployment. On the Pin_Defect_Dataset, SADL–YOLOv11 attains 99.5% mAP@0.5 and 87.8% mAP@0.5:0.95. Under adverse imaging on the Pin_Low_Quality_Dataset (low light, blur/over-exposure, and very small targets), it achieves 97.0% mAP@0.5 and 73.3% mAP@0.5:0.95. Cross-domain transfer to PKU-Market-PCB confirms generalisation: mAP@0.5 matches YOLOv11 (78.5%) and mAP@0.5:0.95 improves by +3.2 points. The detector sustains 66.8 FPS on an RTX 3090 and 18–28 FPS on an RK3568 edge SoC, delivering a practical, high-precision, IoT-ready solution for large-scale power-grid inspection. Source code of proposed SADL-YOLOv11 model and related data-sets are available at the following link: https://github.com/haobai816/ultralytics-yolo11-Pin-Pro_SADL-YOLOv11.git.