关键词: 绝缘子, Yolo v4 模型, 深度可分离卷积块, Mobilenet 网络

Abstract: Convolutional neural network model has the disadvantages of large volume, high computation and poor performance in small and resource limited embedded platform. The existing lightweight model can not take into account the detection speed and accuracy. The mainstream target detection algorithm Yolo v4 is selected to lighten the model, and the mobilenet network and depthwise deparable convolution are used in Yolo v4 model. The results show that compared with the original Yolo v4 model, the improved Yolo v4 model of different mobilenet networks can process an image about 19 ms faster on average, and the accuracy rate can reach more than 92% . The accuracy rate of the improved Yolo v4 model with mobilenet v3 as the backbone feature extraction network is 95. 13% , which is 2. 99% higher than of the original Yolo v4 model. The parameter of this model is about 1 / 6 of Yolo v4 model, and the model can process a patrol image 20 ms faster than the original Yolo v4 model. Insulator is an important part of transmission line, The identification of insulators in many images can help to analyze the operation of transmission lines.

Key words: insulator; , Yolo v4 model; , deep separable convolution block; , mobilenet networks