Enhanced insulator fault detection using optimized ensemble of deep learning models based on weighted boxes fusion

Abstract

Fault identification in transmission line insulators is essential to keep the power system running. Using deep learning-based models combined with interpretative techniques can be an alternative to improve power grid inspections and increase their reliability. Based on that consideration, this paper proposes an optimized ensemble of deep learning models (OEDL) based on weighted boxes fusion (WBF), called OEDL-WBF, to enhance the fault detection of power grid insulators. The proposed model is hypertuned considering a tree-structured Parzen estimator (TPE), and interpretative results are provided using the eigenvector-based class activation map (Eigen-CAM) algorithm. The Eigen-CAM had better results than Grad-CAM, Activation-CAM, MaxActivation-CAM, and WeightedActivation-CAM. The multi-criteria optimization of the structure by TPE ensures that the appropriate hyperparameters of the you only look once (YOLO) model are used for object detection. With a mean average precision (mAP)@[0.5] of 0.9841 and mAP@[0.5:0.95] of 0.9722 the proposed OEDL-WBF outperforms other deep learning-based structures, such as YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12 in a benchmarking. The Eigen-CAM further helps to interpret the outcomes of the model.