Insulator defect detection algorithm based on improved YOLOv5
Tang Jing1,2， Yu Minghui1， Wu Minghu1,2， Yang Chengjian1
(1. Hubei University of Technology， School of Electrical and Electronic Engineering， Wuhan 430072， China；2. Hubei University of Technology， Solar Energy Efficient Utilization and Storage Operation Control Hubei key Laboratory， Wuhan 430068， China)
Abstract：Insulator defect detection plays an important part in the inspection process of the power system. To improve the accuracy of insulator defect detection， a new detection method， YOLOv5t， was proposed based on the YOLOv5 network. The YOLOv5t method was able to increase the accuracy while assuring the speed of network detection. Based on the YOLOv5s network model， theTtriplet Attention was added to the backbone network of the model， and different feature channels were given different weights to improve the detection accuracy of the network. CIoU loss function is used to calculate the regression loss of the network to improve the convergence speed of network. At the same time， soft-NMS is used as the network prediction result processing method to reduce the missed detection rate. Experimental results show that the accuracy， recall and mean accuracy of YOLOv5t in insulator defect detection are 97.2%， 98% and 99.1%， respectively. In comparison to YOLOv5s， YOLOv5t had better accuracy， recall and mean accuracy， which were increased by 0.9%， 5.1% and 2.1%， respectively. And the detection speed is not affected.
2022, Vol. 56 
