Insulator mechanical damage dataset

Regular and rigorous inspection of outdoor insulators is essential for uninterrupted power grid operation. Recent advances in computer vision enabled replacing conventional subjective, costly, and inefficient visual insulator inspection with automated diagnosis from unmanned aerial vehicle (UAV) taken images. In this study, advanced computer vision algorithms, namely, family of YOLOv3 and YOLOv5 architectures, are trained and compared for classification of frequently encountered insulator mechanical faults from UAV images. Hence, a dataset of 8886 insulator images under normal, bird pecking, cracking, and missing cap conditions is collected to train and evaluate our classifiers. In model selection, YOLO models are compared using architectural complexity (number of parameters) and mean average precision at intersection over union (IOU) thresholds from 0.5 to 0.95 (mAP@0.5:0.95). According to model selection results, YOLOv5x and YOLOv5n are the best models in terms of mAP@0.5:0.95 and complexity. Models evaluation using test set reveals that YOLOv5x and YOLOv5n achieved remarkable performance of 95.8% and 90.9% in terms of mAP@0.5:0.95, respectively. Although YOLOv5x reported higher mAP@0.5:0.95, YOLOv5n requires ~41 times less memory and ~49 times less floating-point operations for image classification at the expense of ~5% reduction in mAP@0.5:0.95, which makes YOLOv5n an attractive option for resource-constrained hardware such as UAVs