Research on a Transmission Line Insulator Defect Detection Algorithm Based on Small Sample Learning

The stable operation of power equipment is an important prerequisite for ensuring production and life. As an important power insulation equipment, the traditional insulator defect detection algorithm often requires a mount number of defect samples for model training. However, in reality, due to the low occurrence rate of insulator defects, there is a serious shortage of defect image datasets available for model training, which often leads to problems such as inability to train, overfitting, and low accuracy of the model. In order to address this issue, this paper proposes an improved YOLOv7 insulator defect detection algorithm under small sample conditions. Firstly, the CBAM attention mechanism is added to the YOLOv7 backbone network to improve the feature extraction ability of the model under complex backgrounds and small insulator targets; Secondly, introduce meta learning technology into the network model to enhance the algorithm's accurate detection ability under small sample conditions. The experimental results show that the improved YOLOv7 algorithm can accurately identify insulator defects in transmission lines on a small-scale raw dataset, with mAP reaching 96.8%, Precision reaching 95.3%, and Recall value reaching 93.6%. The improved method has broad application prospects in the detection of insulators in power systems.