A Systematic Literature Review of Waste Identification in Automatic Separation Systems

IF 4.6 Q2 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Recycling Pub Date : 2023-11-02 DOI:10.3390/recycling8060086
Juan Carlos Arbeláez-Estrada, Paola Vallejo, Jose Aguilar, Marta Silvia Tabares-Betancur, David Ríos-Zapata, Santiago Ruiz-Arenas, Elizabeth Rendón-Vélez
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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.
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自动分类系统中废物识别的系统文献综述
适当的废物分类对回收是必不可少的。然而,准确识别废物可能是一项挑战,尤其是在现实环境中。在本研究中,进行了系统的文献综述(SLR),以确定间接分离系统中用于废物识别的物理使能器(传感器和计算设备)、数据集和机器学习(ML)算法。这篇综述分析了55项研究,遵循了Kitchenham指南。SLR确定了废物分类系统的三个自治水平:完全、中等和低。边缘计算设备在数据处理方面的应用最为广泛(17项研究中有9项)。五种类型的传感器用于废物识别:电感式、电容式、基于图像的、基于声音的和基于重量的传感器。基于可见图像的传感器在文献中是最常见的。单一分类是最流行的数据集类型(65%),其次是边界框检测(22.5%)。卷积神经网络(cnn)是垃圾识别中最常用的ML技术(26篇文章中有24篇)。其中一个主要结论是,废物识别面临着现实世界复杂性、数据集中数据有限以及缺乏详细废物分类的挑战。未来的废物识别工作应侧重于在非受控环境中的部署和测试,扩展系统功能,探索传感器融合。
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来源期刊
Recycling
Recycling Environmental Science-Management, Monitoring, Policy and Law
CiteScore
6.80
自引率
7.00%
发文量
84
审稿时长
11 weeks
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