KCDet: Keypoint Clustering Detector for Small Bolt Defect Detection in Power Line Inspection

This is the authors' abstract. We don't add key points for this paper.

The UAV-based power line intelligent inspection using vision-based instrumentation and measurement plays a crucial role in ensuring the safety of transmission lines. However, small object defects, such as bolt defects, are difficult to detect due to their minimal size in captured images, resulting in low detection rates. To address this issue, this paper proposes a novel keypoint clustering detector (KCDet), consisting of a keypoint detection network, an efficient clustering method, and an object detector, to accurately identify bolt defects. Firstly, a keypoint detection network based on the high-resolution heatmap is proposed, treating bolts as points for localization, which significantly improves the accuracy of bolt localization. Secondly, an efficient clustering method is introduced to cluster the localized bolts, and the cluster regions are cropped from the original image to obtain high-resolution sub-images. Finally, an improved YOLOv11 is presented to precisely identify bolt defects from these sub-images. Experimental results demonstrate that the proposed KCDet increases accuracy by over 20% compared to the single-model-based YOLOv11s algorithm while maintaining high inference speed, significantly enhancing the accuracy of small-sized bolt defect detection in power transmission line inspections.