DeepScan: Revolutionizing Garbage Detection and Classification with Deep Learning

With the rapid increase in human household waste due to urbanization, garbage classification has become a critical issue in environmental protection. The traditional manual classification method is no longer sufficient to meet the growing demand, highlighting the need for an efficient and accurate garbage classification and identification system. This paper proposes a garbage classification and recognition system that uses a convolution neural network for feature extraction and classification. The system was trained and tested using datasets containing five types of garbage. The experimental results show that the proposed system achieves an accuracy of 90.3%, outperforming traditional machine learning methods. Furthermore, confusion matrix analysis and feature visualization provide additional evidence of the system’s effectiveness and interpretability. This deep learning-based garbage classification and recognition system has significant potential for practical use in urban environmental management and garbage classification and treatment enterprises.