YOLO‐EPDS: A Small Object Detection Algorithm for Power Transmission Line Nut Spacing Looseness

ABSTRACT To address the challenges of feature loss, inaccurate localization, and false or missed detections in small‐object detection of loosened and spaced nuts in power transmission lines, this study proposes an enhanced detection model, you only look once‐EPDS (YOLO‐EPDS), built upon an improved YOLOv9 framework. A RepNCSPELAN4_EMA module is integrated into the backbone network to incorporate a multi‐scale attention mechanism, enhancing the extraction of subtle nut texture features via cross‐space interactions and parallel multi‐branch feature recalibration. SPD‐Conv modules replace conventional downsampling layers in the backbone, effectively preserving spatial details in feature maps. Additionally, a RepNCSPELAN4_DCNv4 module employs dynamic deformable convolutions (DCNv4) to improve adaptability to geometrically deformed objects. The shape‐IoU loss function is utilized to optimize bounding box regression for small objects. Experimental results indicate that the proposed model achieves a mAP@50 of 79.7% on a self‐constructed transmission line nut dataset, outperforming the baseline by 4.3%. These enhancements synergistically increase confidence scores while reducing false‐positive and false‐negative rates, demonstrating superior capability in extracting defective features of loosened nuts and substantially improving the reliability of transmission line inspection.