YOLOv5 garbage classification method with GhostNet - Li Yao; Hu Junguo; Le Yang (Dianzi Jishu Yingyong, 2024)
Garbage classification is an important part of building ecological civilization. To solve the problem that heavyweight models are difficult to deploy to mobile devices, an improved garbage image classification method based on YOLOv5 network is proposed. The backbone network fused with GhostNet is used to replace the traditional convolutional operation with linear operation, which reduces the number of parameters of the model and improves the model inference speed. By adding an improved version of channel attention module to the network, the important channel features are strengthened and more deep-level feature information is obtained. The weighted boundary fusion method is used to improve the localization accuracy of the detection frame. It is experimentally demonstrated that the method improves the accuracy by 8.5%, reduces the parameter quantity by 46.7%, and increases the average inference speed by 1.22 ms in the homemade dataset compared with the original model, achieving a comprehensive improvement of accuracy and inference speed.