Garbage Detection Algorithm Based on Deep Learning

Rapid and accurate detection of garbage has become a hot issue in the field of computer vision. In this paper, we propose an intelligent garbage detection algorithm to improve the accuracy and speed of garbage detection. For the self-made garbage dataset, to reduce the annotation workload and the problem of missing annotation and wrong annotation, YOLOv4 combined with self-learning strategy is used to realize the training of garbage detection model and improve the accuracy of detection tasks. At the same time, the fast NMS algorithm is introduced to accelerate the prediction process and improve the model prediction speed. The experimental results show that the detection effect of the algorithm in this paper is significant, and it can effectively identify six different kind of garbage, with mean Average Precision (mAP) of 95.36% and a detection time of 25ms for a single image, which is expected to be applied to the recognition and detection of waste targets in industrial conveying scenarios.