Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

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Published
2025-01-09
DOI
https://doi.org/10.1109/ipas63548.2025.10924491
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
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article

Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Dorra Sellami, Ahmed Fakhfakh, Jihen Frikha Elleuch, Amal Remili et al.
Advanced Manufacturing and Logistics Optimization
article

Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Dorra Sellami, Ahmed Fakhfakh, Jihen Frikha Elleuch, Amal Remili, Mouna Zouari Mahdi
article en

Abstract

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

Digital Research Centre of Sfax (TN)
Responsible consumption and production
Openalex Percentile: Top 93%
Advanced Manufacturing and Logistics Optimization
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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management — Dorra Sellami, Ahmed Fakhfakh, et al. · (2025) | TGRS Research Map | TGRS