Enhanced YOLOv5l model for precise insulator defect detection
Due to the characteristics of UAV power image,such as large variation of insulator device scale,complex transmission line background and small insulator defect target,the recognition accuracy of traditional target detection algorithm is not high. This paper proposes an improved network model based on YOLOv5l,CA,Transformer coding block and multi-scale integration,which can better improve the accuracy of insulator defect detection on large-scale change images,improve the ability of multi-type insulator defect identification under complex background,and solve the problem of small insulator defect detection. Extensive training and verification experiments were conducted on a comprehensive dataset provided by a power grid company. The experimental results demonstrate that the optimized model achieves a significant enhancement over the original YOLOv5l model,with an increase of 8. 9％ in accuracy rate,4. 4％ in recall rate,and 3. 5％ in average accuracy. Moreover,comparative analysis with the state-of-the-art detection model confirms the effectiveness and reliability of our improved model for insulator defect detection.
万方数据
维普
