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

The unmanned aerial vehicle (UAV)-based power line intelligent inspection using vision-based instrumentation and measurement plays a crucial role in ensuring the safety of trans mission 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 article 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. First, 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. Second, 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 subimages. Finally, an improved YOLOv11 is presented to precisely identify bolt defects from these subimages. Experimental results demonstrate that the proposed KCDet increases accuracy by over 20% compared with 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.