Cap-Count Guided Weakly Supervised Insulator Cap Missing Detection in Aerial Images

The vision-based detection of cap missing on insulator in aerial images is an extremely challenging task. Previous approaches have achieved promising performances. However, most of them include two stages of insulator localization and cap detection, which require large-scale insulator and cap annotations. In this article, we propose a cap-count guided weakly supervised approach, which only needs insulator bounding box annotations. The proposed method first generates cap annotations automatically by giving the insulator bounding boxes and the number of caps. Then, we use a deep learning model to detect insulator strings and caps simultaneously. Finally, we design an efficient cap missing detection strategy by using caps geometric constraints to measure the position of caps. We evaluate the proposed approach on our collected large-scale aerial image dataset. The experimental results show that the proposed method can accurately detect the insulator strings and caps with any angle from an aerial image and further recognize the missing cap on insulator. The precision and recall reach 92.86% and 86.67% respectively, which achieves competitive detection results.