MID-YOLO: An enhanced YOLOv8-based method for multi-type insulator defect detection

Abstract

Insulators in overhead transmission systems are susceptible to the adverse effects of harsh weather and other conditions in outdoor environments, posing threats to the safety and stability of the transmission systems. Currently, deep learning-based object detection algorithms have made significant strides in the field of insulator defect detection. However, existing methods mainly tackled insulator broken, overlooking the nuanced detection of complex flashover issues, which require a robust detection network, this research advances insulator defect detection by proposing a novel algorithm based on the YOLOv8 framework, aimed at accurately localizing diverse insulator faults within complex natural scenes. Key innovations include a new neck feature fusion structure and a detail-aware module, which together enhance the model’s proficiency in identifying small defects. These modifications have led to notable improvements: a 3.1% increase in average accuracy and an 8.2% uplift in recall rate. Specifically, the CFM module’s efficient fusion and TCB’s channel weighting significantly boost feature extraction, the MRB’s multi-branch strategy increases recall while reducing parameters, and the DAB’s attention mechanism sharpens detail perception. The developed MID-YOLO model matches the performance of more parameter-heavy counterparts, offering a practical solution for insulator defect detection.