Garbage Classification System with YOLOV5 Based on Image Recognition

Abstract

At present, the main technology of garbage identification and classification is the use of traditional machine vision algorithm or the use of sensors for screening and identification of garbage, in garbage sorting, the first accurate identification and classification of garbage is very necessary. By collecting various types of garbage pictures and building detection data sets, we adopt the garbage identification and detection algorithm based on YOLO-V5 and use data enhancement to improve the robustness of the model, to achieve fast and accurate identification of different types of garbage. Experimental results show that this method has high accuracy, short time consumption, and good robustness.