Optimizing Waste Management with Advanced Object Detection for Garbage Classification

TL;DR

This paper explores the use of YOLO V5 object detection models to improve waste classification accuracy, aiming to enhance recycling and waste management efficiency through AI-driven sorting systems.

Contribution

It demonstrates the application of YOLO V5 for classifying different waste types, showcasing its effectiveness in environmental waste management.

Findings

YOLO V5 accurately identifies multiple waste categories.

AI-based sorting can potentially improve recycling efficiency.

The approach offers a scalable solution for waste classification.

Abstract

Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal. Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively. These models can be applied to sorting systems or possibly waste collection robots that may produced in the future. AI models have grown significantly at identifying objects through object detection. This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing. The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.