Region-Based Active Learning for Insulator Defect Diagnosis Using Aerial Images of Electric Transmission Networks

Deep learning-based algorithms are considered an efficient solution to carry out the insulator defect diagnosis task based on the aerial images captured by Unmanned Aerial Vehicles (UAVs) for electric power systems. However, the sufficient and accurate annotations of image samples required by deep learning-based models can be costly or not feasible in practice. This paper proposed an active learning-based solution for insulator defect diagnosis of electric transmission networks. The proposed solution aims to identify the most valuable samples and assign ground-true labels to significantly reduce the labeling effort. Specifically, Batch Active learning by Diverse Gradient Embedding (BADGE) strategy is adopted for sampling and GradCAM++ is used to extract the key regions of image samples iteratively. Then, a region-sample pair construction method is proposed during the labeling stage enabling the model to focus on the most discriminative regions based on a well-designed loss function. The proposed solution is extensively assessed through experiments and the results demonstrate that F1 scores of four popular CNN models trained with 1/3 of the total samples can be increased by up to 2.0% compared to the fully-labeled baseline solutions.