{"title":"Wimplebin: an AI-based recycle bin for a better waste management","authors":"Jiacang Ho, JongHyuk Lee, HyoungSuk Kim","doi":"10.1007/s10163-024-02145-9","DOIUrl":null,"url":null,"abstract":"<div><p>Advanced artificial intelligence (AI) technologies have bestowed numerous advantages upon our daily lives. Despite the ongoing efforts of various institutions urging responsible waste distribution for the preservation of our planet, completely resolving the waste problem remains a formidable challenge. This paper endeavors to present a solution through the integration of AI into waste distribution systems. We introduce WimpleBin, an AI-based recycle bin, designed to accurately classify waste streams following training with machine learning algorithms. Utilizing the YOLOv5 framework, we train WimpleBin with the collected data to accomplish our objectives. The 81% accuracy achieved in real-world scenarios demonstrates WimpleBin’s impressive ability to effectively categorize different types of waste.</p></div>","PeriodicalId":643,"journal":{"name":"Journal of Material Cycles and Waste Management","volume":"27 1","pages":"584 - 596"},"PeriodicalIF":2.7000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Material Cycles and Waste Management","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10163-024-02145-9","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Advanced artificial intelligence (AI) technologies have bestowed numerous advantages upon our daily lives. Despite the ongoing efforts of various institutions urging responsible waste distribution for the preservation of our planet, completely resolving the waste problem remains a formidable challenge. This paper endeavors to present a solution through the integration of AI into waste distribution systems. We introduce WimpleBin, an AI-based recycle bin, designed to accurately classify waste streams following training with machine learning algorithms. Utilizing the YOLOv5 framework, we train WimpleBin with the collected data to accomplish our objectives. The 81% accuracy achieved in real-world scenarios demonstrates WimpleBin’s impressive ability to effectively categorize different types of waste.
期刊介绍：
The Journal of Material Cycles and Waste Management has a twofold focus: research in technical, political, and environmental problems of material cycles and waste management; and information that contributes to the development of an interdisciplinary science of material cycles and waste management. Its aim is to develop solutions and prescriptions for material cycles.
The journal publishes original articles, reviews, and invited papers from a wide range of disciplines related to material cycles and waste management.
The journal is published in cooperation with the Japan Society of Material Cycles and Waste Management (JSMCWM) and the Korea Society of Waste Management (KSWM).
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