Real-Time Solid Waste Sorting Machine Based on Deep Learning

Abstract

The collection and separation of solid waste represent crucial stages in recycling. However, waste collection currently relies on static trash bins that lack customization to suit specific locations. By integrating artificial intelligence into trash bins, we can enhance their functionality. This study proposes the implementation of a sorting machine as an intelligent alternative to traditional trash bins. This machine autonomously segregates waste without human intervention, utilizing deep learning techniques and an embedded edge device for real-time sorting. Deploying a convolutional neural network model on a Raspberry Pi, the machine achieves solid waste identification and segregation via image recognition. Performance evaluation conducted on both the Stanford dataset and a dataset we created showcases the machine's high accuracy in detection and classification. Moreover, the proposed machine stands out for its simplicity and cost-effectiveness in implementation.