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
{"title":"A Systematic Literature Review of Waste Identification in Automatic Separation Systems","authors":"Juan Carlos Arbeláez-Estrada, Paola Vallejo, Jose Aguilar, Marta Silvia Tabares-Betancur, David Ríos-Zapata, Santiago Ruiz-Arenas, Elizabeth Rendón-Vélez","doi":"10.3390/recycling8060086","DOIUrl":null,"url":null,"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.","PeriodicalId":36729,"journal":{"name":"Recycling","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Recycling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/recycling8060086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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
期刊最新文献
Quality-Driven Allocation Method to Promote the Circular Economy for Plastic Components in the Automotive Industry Silicon Kerf Recovery via Acid Leaching Followed by Melting at Elevated Temperatures An Investigation into Sustainable Solutions: Utilizing Hydrated Lime Derived from Oyster Shells as an Eco-Friendly Alternative for Semiconductor Wastewater Treatment Environmental and Economic Forecast of the Widespread Use of Anaerobic Digestion Techniques Concentration of Silver from Recycling of Fine Powder of Wasted Videogame Printed Circuit Boards through Reverse Froth Flotation and Magnetic Separation Processes
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1