Research on New Energy Insulator Defect Detection Method Based on Improved YOLOv5

Aiming at the problem of low recognition accuracy of insulator defects in transmission lines, this study proposes a transmission line insulator defect detection method based on improved YOLOv5. Firstly, the ECA attention mechanism is introduced into YOLOv5 to reduce the interference of irrelevant features and improve the feature extraction ability of the model for detection targets. Then, the detection head was combined with the Adaptive Spatial Feature Fusion (ASFF) module to adaptively adjust the weights of feature maps of different scales in fusion, and make full use of feature information of different scales to improve the detection accuracy of the model. The experimental results show that compared with the basic YOLOv5 model, the improved method proposed in this study improves the indicators [email protected] and [email protected]:0.95 by 2.2% and 1.9% respectively, which can locate and identify the detection target more accurately, verifying the effectiveness of the improved method.