A Garbage Intelligent Classification and Recycling System Based on Deep Learning

Abstract

Garbage classification, if not properly implemented, may lead to environmental pollution during recycling. In order to overcome this problem effectively, an intelligent garbage classification and recycling system based on convolutional neural network (CNN) is introduced. In this paper, a well-designed deep network structure is used to optimize the model parameters, and the powerful computing power of graphics processing unit (GPU) is used to realize parallel processing and batch processing, which aims to improve the classification accuracy and processing speed of the system. According to the results of the study, the CNN-based garbage classification system demonstrated extremely high performance, with classification accuracy of up to 99% and a minimum of 94%. The system design significantly improves the accuracy of garbage classification. With the help of automatic feature learning and the capability of deep network architecture, the system can more accurately identify the key features in the garbage image, thus ensuring the reliability of the classification results and laying the foundation for the efficient recycling of garbage.