Application of Improved YOLOv8 Network in Insulator Defect Detection
The problem of small and scattered insulator defect targets has been affected the improvement of detection precision.In response to the shortcomings of existing insulator defect detection methods,the algorithm adds a small tar-get detection layer to the YOLOv8 network based on the attention mechanism and multi-scale fusion,and adds the self-at-tention and convolution(ACmix).A weighted bi-directional path aggregation network(Bi-PANet)is proposed instead of path aggregation network(PANet)to prevent the loss of original features during feature fusion and improve the fusion of multi-scale target features.Using Wise-IOU as the regression loss function,the influence of low-quality annotations is re-duced and the network convergence speed is accelerated.Experiments are conducted to detect normal insulators and dropped string insulators on power lines in aerial images,and the results show that the mean average accuracy of the proposed detection method reaches 93.2%,which proves that the improved model is able to better recognize insulator defects.
NETL
NSTL
万方数据
