Insulators are essential components in high-voltage transmission lines and require regular inspection to ensure reliable power delivery. Traditional manual inspection methods are inefficient and labor intensive, highlighting the need for intelligent and automated solutions. In this study, we propose a missing insulator detection method that integrates Unmanned Aerial Vehicle (UAV) imaging with deep learning techniques. Firstly, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) is employed to detect and localize insulators in aerial images. Secondly, the localized insulators are segmented using an improved U-Net to reduce background interference. A bounding box regression approach is adopted to obtain the minimum enclosing rectangles, and the insulators are aligned vertically. Adaptive thresholding is then applied to extract binary images of the insulators. These binary images are further transformed into defect curves, from which missing insulators are identified based on curve distribution. To address the limited availability of labeled samples, a transfer learning-based strategy is adopted to improve model generalization. A dataset of glass insulators was collected using a DJI M300 UAV equipped with an H20T camera along a 330 kV overhead transmission line. On the collected UAV insulator dataset, the proposed method achieved an AP@0.5 of 99.85% and an average IoU of 88.56% for insulator string detection, while the improved U-Net achieved an mIoU of 89.73% for insulator string segmentation. Outdoor flight experiments further verified performance under varying backgrounds and illumination conditions in our UAV inspection scenarios.
Zhang et al. (Wed,) studied this question.