A lightweight YOLOv5 garbage detection and classification method

Abstract

Aiming at the problems of unclear, difficult, and inefficient classification of traditional manual waste, and the difficulty of deploying large existing garbage classification network models, a lightweight garbage detection and classification network S-YOLOv5 is designed based on YOLOv5s. First, a garbage dataset containing 18 types of common household garbage is constructed and labeled according to the principles of garbage classification; secondly, a module combining shufflenetv2 and CoordAttention was introduced to replace the YOLOv5s backbone network, and the ReLU activation function in the shufflenet module was substituted by FReLU; finally the PANet structure was replaced by the BiFPN structure, so as to reduce the model complexity and achieve lightweight while maintaining a high mAP. The experimental results show that the size of S-YOLOv5 is only 2.6MB, which is about 1/6 of the original network size, and the mAP is 80.2%. The size of the proposed network is reduced while maintaining high accuracy, making it more suitable for deployment in smart devices.