Research on insulator detection algorithm based on improved YOLOv5
Aiming at the low accuracy of insulator defect recognition,this paper proposes an improved YOLOv5 al-gorithm,which introduces the dynamic convolution module ODConv(Omni-Dimensional Dynamic Convolution)in the feature fusion part of the original model.It enhances the feature extraction ability of the model to the target through the parallel multi-dimensional attention mechanism strategy.The experimental results show that the im-proved algorithm improves the recall rate by 2.2％and the accuracy by 3％compared with the original algorithm,The average accuracy(MAP)is improved by 2％,and the speed reaches 172 frames/s on NVIDIA GeForce RTX 3060 6G video memory device.Compared with many mainstream target detection algorithms,the algorithm in this paper has better comprehensive performance,which can provide technical reference for insulator fault patrol inspec-tion of transmission lines.
万方数据
维普
