Automated Garbage Classification Using Convolutional Neural Networks

Automated Garbage Classification Using Convolutional Neural Networks

Mohith A S

Abstract
The increase in population has significantly impacted urban areas, particularly in the management of waste. In India, as per the National Waste Management Information System (SIPSN) 2021 data, the waste volume reached 28.57 million tons per year, with only 64.52% of it being managed effectively. The challenge lies in the lack of knowledge and awareness among the community regarding proper waste segregation. This study presents a deep learning-based approach using ShuffleNet v2 to classify waste into four categories: plastic, organic, paper, and others. Utilizing a dataset of 6601 waste images, the convolutional neural network (CNN) model demonstrated high accuracy in classifying the waste, potentially aiding in efficient waste management and recycling efforts.

Keywords
Deep Learning, Convolutional Neural Networks (CNN), Garbage Classification, Waste Management, Image Processing, ShuffleNet v2, Machine Learning, Automated Waste Segregation, Real-Time Waste Detection, Environmental Sustainability.