Power equipment detection algorithm based on immune deep network
With the rapid development of the power system,ensuring the stable and safe operation of power equipment has become an important task.Aiming at the problem of detection of working state of insulators and transformer bushings,based on the theory of biological immune intelligence and the current deep learning model,a new immune depth network is proposed,and the deep residual network is used to extract features,SIoU calculation is used to detect anchor frame loss,and three SPP structures are used for multi-scale feature fusion.Through experimental comparative analysis,the MAP of the immune deep network can reach 80.97％,which is 4.55％higher than that of the YOLOV3 model,and by comparing with the detection effect of Prewitt,Kmeans,Otsu,and YOLOv3,the immune deep network not only has no missed detection and false detection,but also has a better detection accuracy than other models.
万方数据
维普
