Research on Insulator Defect Detection Based on Improved YOLOv7

Insulators are crucial components of the transmission and distribution networks, and they are prone to self-explosion under the influence of bad weather. Self-explosion defect diagnosis of insulators has always been an important task in power inspection. To solve this problem, an insulator defect detection algorithm model based on YOLOv7 algorithm is introduced in this article. Firstly, the attention mechanism is integrated into the network framework of the algorithm to enhance the algorithm’s capacity for feature extraction. BiFPN was used for feature fusion to reduce the computational load. Secondly, the improved loss function optimization algorithm is used to train the process. According to the experimental findings, the improved detection model’s accuracy and average accuracy are 97.2% and 97.4%, respectively.