Towards the Urban Future: A Novel Trash Segregation Algorithm Based on Improved YOLOV4

摘要

The ecological imbalance caused by environmental pollution has gradually become a thought-provoking topic. In addition, the development of global smart cities urgently requires the emergence of a powerful smart waste management system which can realize trash segregation. Therefore we focus on the this issue and proposed a novel method for trash classification based on deep learning strategy. In order to realize the efficiency of real-time outdoor trash detection by robot, we adopt a lightweight GhostNet as backbone of detection network. The network was trained in our self-made dataset which includes 4 kinds of object(namely : plastic bottle, glass bottle, metal can, carton). Moreover, for comparison and evaluation, we set up a comparative experiment. The experimental results show that the improved version of the YOLOv4 method proposed has improved detection performance compared to the original YOLOV4 algorithm, and has produced satisfactory Generalization performance in different types of trash.