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Journal of Material Cycles and Waste Management

Erschienen in:

14.03.2024 | ORIGINAL ARTICLE

A novel recycling method using machine vision to assist in the processing of stacked waste fans

verfasst von: Ning Wang, Qunbiao Wu, Mingqiang Wang, Defang He, Haifeng Fang

Erschienen in: Journal of Material Cycles and Waste Management | Ausgabe 3/2024

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Abstract

With the increasing amount of Waste Electrical and Electronic Equipment (WEEE), waste electric fans are an important component that cannot be ignored due to their variety and high recycling value. Utilizing intelligent methods for waste recycling has become a prominent topic in contemporary society. However, there is a lack of sophisticated datasets and algorithms in the field of waste electric fan recycling. In order to help automatically recycle a large number of piled-up electric fans, this paper proposes a novel Mrcnn-Stafans instance segmentation model based on a modified mask R-CNN. In addition, a stacked electric fan-TRASH dataset that is closer to the actual recovery situation is constructed. Experimental results show that the proposed improved algorithm achieves 98.161% accuracy while optimizing the number of model parameters. The focus of this work is to help identify recycled WEEE fans using visual recognition techniques, thus helping solve environmental problems.

Vorheriger Artikel Investigation of thermal desorption characteristics and pilot-scale study of polycyclic aromatic hydrocarbons (PAHs)-contaminated soil during cement manufacturing

Nächster Artikel Effect of temperature and additives on recycled waterborne coating preparation and performance for construction and demolition waste reutilization

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Titel: A novel recycling method using machine vision to assist in the processing of stacked waste fans
verfasst von: Ning Wang
Qunbiao Wu
Mingqiang Wang
Defang He
Haifeng Fang
Publikationsdatum: 14.03.2024
Verlag: Springer Japan
Erschienen in: Journal of Material Cycles and Waste Management / Ausgabe 3/2024
Print ISSN: 1438-4957
Elektronische ISSN: 1611-8227
DOI: https://doi.org/10.1007/s10163-024-01916-8

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