A Systematic Literature Review of Waste Identification in Automatic Separation Systems

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), dataset...

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Bibliographic Details
Published in:Recycling (Basel) 2023-12, Vol.8 (6), p.86
Main Authors: Arbeláez-Estrada, Juan Carlos, Vallejo, Paola, Aguilar, Jose, Tabares-Betancur, Marta Silvia, Ríos-Zapata, David, Ruiz-Arenas, Santiago, Rendón-Vélez, Elizabeth
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
automatic
Classification
Data processing
Datasets
Edge computing
Environmental impact
Identification
Internet of Things
Literature reviews
Machine learning
Medical wastes
Methods
Multisensor fusion
municipal
Municipal solid waste
Neural networks
Refuse and refuse disposal
Resource recovery
segregation
Sensors
Separation
Support vector machines
Systematic review
Technology application
waste
Waste identification
Waste materials
Waste sorting
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Summary: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.
ISSN:2313-4321
2313-4321
DOI:10.3390/recycling8060086