Uncertainty-aware accurate insulator fault detection based on an improved YOLOX model

The surveillance and inspection of power line insulators, which act as essential components for the connection and insulation of power lines, play pivot roles in the daily maintenance of the power grid system since the failure of power line insulators could cause abrupt power cuts and accidents. Vision-assisted Unmanned Aerial Vehicle (UAV) technology has recently become an efficient and economical solution for automatic insulator fault inspection and demonstrated considerable accuracy with deep learning-based detection methods. However, current detection methods cannot predict and handle the uncertainty of insulator detection, and their capabilities are limited. This paper presents a novel deep learning-based methodology, namely YOLOD, to address the uncertainty issue by applying a Gaussian prior to the detection heads of YOLOX, the current state-of-the-art model of compact object detectors, for both bounding box regression and corresponding uncertainty estimation. Then, the estimated uncertainty scores are utilized to refine the bounding box prediction and further improve the robustness of the detection. Finally, in the comprehensive experiments, the proposed YOLOD model outperforms other benchmark models on a public insulator dataset and achieves the highest average precision (73.9%), which is 2.1% higher than that of YOLOX. Thus, the effectiveness and superiority of the proposed method for robust insulator defect inspection are validated.