In order to promote the degree of autonomy and intelligence of garbage classification, the garbage bin need be equipped with visual sensor and intelligent hardware carrying effective garbage detection and classification algorithm. To meet this demand, an improved garbage identification and classification algorithm based on YOLOv3 is proposed. First, MobileNetv3 network is introduced to replace Darknet53, the backbone network of YOLOv3, and spatial pyramid pooling structure is added to reduce the computational complexity of the network model and ensure the accuracy of the model. Second, four different scales are used to enhance the detection ability of the model to small targets. Then, the loss function of the original YOLOv3 model is replaced by the complete intersection over union (CIOU) loss function to improve the accuracy of the network model. Finally, a household trash can test platform is built, and the proposed algorithm is transplanted to the edge computing module NVIDIA Xavier NX. The experimental results show that the average accuracy of the proposed optimization algorithm is consistent on the server and NVIDIA Xavier NX platform in the self-made garbage dataset,reaches 72.1%, which is 4.9 percentage point higher than that of YOLOv3 and 1.6 percentage point lower than that of YOLOv4; detection speed is 74,19 frame/s, which is much higher than 43,8 frame/s of YOLOv3 algorithm and 50,11 frame/s of YOLOv4 algorithm, indicating that proposed algorithm meets the requirements of edge computing equipment and has potential application value.