DINS: A Diverse Insulator Dataset for Object Detection and Instance Segmentation

Intelligent defect detection of insulators is faster, more accurate, standardized, and cheaper than manual detection with necessary massive inspection work. Insulator datasets are important for training detection models. Nevertheless, public datasets are scarce and lack variety, which hampers improving detection accuracy and achieving industrial-grade accuracy. We construct a comprehensive beyond the current insulator dataset—the diverse insulator dataset (DINS). DINS contains over 10 000 insulator images involving three insulator types (porcelain, glass, and composite) and defects. We annotate over 25 000 bounding boxes for object detection and 9000 masks, for instance, segmentation. DINS has much more scale and diversity than the current insulator datasets. Eventually, we discussed the effective augmentation methods for DINS and conducted some experiments demonstrating the usefulness of DINS with 98.3% mean average precision (mAP) of object detection and 97.2% mAP of instance segmentation. The datasets are available on GitHub.