Routine evaluation of insulator performance is important for maintaining the reliability and safety of power system operations. The use of unmanned aerial vehicles (UAVs) has been a significant advancement in transmission line monitoring, effectively replacing traditional manual inspection methods. With the rapid advancement of deep learning techniques, methods based on these models for detecting insulator defects have attracted increasing research interest and achieved notable advancements. Nevertheless, existing approaches primarily emphasize constructing sophisticated and intricate network architectures, which consequently lead to greater inference complexity when applied in practical scenarios. On the other hand, foggy scenarios pose challenges for learning algorithms due to difficulties in obtaining and labeling samples, as well as the poor performance of detectors trained on clear-weather samples. This study proposes adaptive enhancement based on YOLO, a framework that has robustness and domain generalization under fog-induced distribution shifts. It optimizes at multiple scales and enhances images as input to a detector in a single pipeline. Experimental results demonstrate improved performance on public UPID and SFID insulator defect datasets, improving insulator defect detection precision without increased computational complexity or inference resources, which is of great significance for advancing object detection in adverse weather. The proposed method achieves real-time performance, with an end-to-end inference speed exceeding 25 FPS and a model-only speed of approximately 38 FPS on 678 images from UPID, demonstrating both practical applicability and computational efficiency.
Kariri et al. (Fri,) studied this question.