A Systematic Literature Review of Waste Identification in Automatic Separation Systems

Author:

Arbeláez-Estrada Juan Carlos1,Vallejo Paola1,Aguilar Jose123,Tabares-Betancur Marta Silvia1,Ríos-Zapata David4ORCID,Ruiz-Arenas Santiago4ORCID,Rendón-Vélez Elizabeth4ORCID

Affiliation:

1. R+D+I in Information and Communications Technologies (Giditic), Universidad EAFIT, Medellin 050022, Colombia

2. Departamento de Computación, Universidad de Los Andes, Merida 5001, Venezuela

3. IMDEA Network Institute, 28918 Madrid, Spain

4. Design Engineering Research Group (GRID), Universidad EAFIT, Medellin 050022, Colombia

Abstract

Proper waste separation is essential for recycling. However, it can be challenging to identify waste materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out to identify the physical enablers (sensors and computing devices), datasets, and machine learning (ML) algorithms used for waste identification in indirect separation systems. This review analyzed 55 studies, following the Kitchenham guidelines. The SLR identified three levels of autonomy in waste segregation systems: full, moderate, and low. Edge computing devices are the most widely used for data processing (9 of 17 studies). Five types of sensors are used for waste identification: inductive, capacitive, image-based, sound-based, and weight-based sensors. Visible-image-based sensors are the most common in the literature. Single classification is the most popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) are the most commonly used ML technique for waste identification (24 out of 26 articles). One of the main conclusions is that waste identification faces challenges with real-world complexity, limited data in datasets, and a lack of detailed waste categorization. Future work in waste identification should focus on deployment and testing in non-controlled environments, expanding system functionalities, and exploring sensor fusion.

Funder

Ministerio de Ciencia Tecnología e Innovación—Minciencias

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Waste Management and Disposal,General Materials Science

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1. WasteInsight: Conjunto de Dados para Detecção e Estimativa de Volume de Resíduos Sólidos Urbanos;Anais do XV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA 2024);2024-07-21

2. Advanced Single-View Image-Based Framework for Volume Estimation in Urban Solid Waste Management;Anais do XV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA 2024);2024-07-21

3. An MRS-YOLO Model for High-Precision Waste Detection and Classification;Sensors;2024-07-04

4. An Efficient Multi-Label Classification-Based Municipal Waste Image Identification;Processes;2024-05-24

5. EcoMind: Web-based waste labeling tool;SoftwareX;2024-05

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