Garbage detection and classification method based on YoloV5 algorithm

In the face of a wide variety and a large number of production and domestic waste, it is a great challenge for the task of automatic detection and sorting of waste. Based on yolov5 algorithm, this paper proposes a method for rapid detection and classification of garbage, trains the model on taco[1] garbage data set, and extracts the location and feature information of garbage through this network model according to the experimental results. In reality, this model can effectively detect the garbage classified by the data set. After testing, the mAP(Mean Average Percision) value of the model reaches 97.62%, the detection accuracy is 95.49%, and the detection speed reaches 5.52fps. Compared with yolov3 network model, which better complete the task of garbage classification and detection. This network model has the necessary technical conditions for the algorithm of waste sorting robots.