AI Based Waste classifier with Thermo-Rapid Composting

TL;DR

This paper introduces a computer vision and deep learning-based waste classification system using YOLOv3 and SVMs, combined with rapid composting techniques, to improve waste segregation efficiency in large cities.

Contribution

It presents a novel integrated approach combining YOLOv3, SVM, and rapid composting for efficient municipal waste classification and biodegradable waste decomposition.

Findings

Enhanced waste classification accuracy using YOLOv3 and SVM.

Faster and more efficient waste segregation process.

Effective decomposition of biodegradable waste with Berkeley Method.

Abstract

Waste management is a certainly a very complex and difficult process especially in very large cities. It needs immense man power and also uses up other resources such as electricity and fuel. This creates a need to use a novel method with help of latest technologies. Here in this article we present a new waste classification technique using Computer Vision (CV) and deep learning (DL). To further improve waste classification ability, support machine vectors (SVM) are used. We also decompose the degradable waste with help of rapid composting. In this article we have mainly worked on segregation of municipal solid waste (MSW). For this model, we use YOLOv3 (You Only Look Once) a computer vision-based algorithm popularly used to detect objects which is developed based on Convolution Neural Networks (CNNs) which is a machine learning (ML) based tool. They are extensively used to extract features from a data especially image-oriented data. In this article we propose a waste classification technique which will be faster and more efficient. And we decompose the biodegradable waste by Berkley Method of composting (BKC)