Garbage Classification Recognition Model Based on YOLOv5 - Zhao, Hongxuan; Zou, Jiaxin (Journal of Theory and Practice of Management Science, 2025)
In response to the national promotion of garbage classification and to assist citizens in effectively sorting and discarding garbage, we have studied an image-based garbage detection and classification model to achieve recognition and detection of garbage. Train a garbage classification model based on YOLOv5s on GPU servers. Then the trained model is deployed to the server, and users take photos and upload them to the server through the WeChat mini program. The server processes the images through the model and returns the processed images to the WeChat mini program. Users can use photos to determine which category garbage belongs to and classify it accordingly. The final trained model can recognize 44 types of garbage, and has good performance in recognition accuracy and response speed.