Garbage detection and classification based on improved YOLOV4

Abstract

Object detection is an important research task in computer vision. With environment problems becoming seriously, garbage detection has become a hot direction in object detection. Though great progress has been made in garbage detection, there still exists challenges for general-purpose detectors with no public datasets and low recognition accuracy rate. Based on YOLOV4 model, this paper proposes an improved YOLOV4 detector for garbage detection. Specifically, CBAM is added to the feature extraction network in order to better extract features in deep networks, and focal function is integrated into the loss function to improve class imbalance. When preparing the data, environment information and random information are added to pictures to simulate the distribution of garbage in real environment, and finally an urban household garbage dataset containing 47 classes of 45,910 images named TrashSet has been produced. Experiments results on TrashSet verify that our detector has a considerable performance, and mAP reaches 97.15%.