Multichannel Convolutional Networks for Classification and Localization of Construction and Demolition Waste
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00384947" target="_blank" >RIV/68407700:21110/25:00384947 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-94600-4_3" target="_blank" >https://doi.org/10.1007/978-3-031-94600-4_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-94600-4_3" target="_blank" >10.1007/978-3-031-94600-4_3</a>
Alternative languages
Result language
angličtina
Original language name
Multichannel Convolutional Networks for Classification and Localization of Construction and Demolition Waste
Original language description
Effective management of construction and demolition waste (CDW) remains a critical concern for environmental sustainability and economic efficiency. Inadequate sorting mechanisms result in subopti-mal resource use and diminished recycling opportunities. This paper presents a sophisticated machine-learning approach that utilizes multi-channel convolutional neural networks (CNNs) to improve CDW sorting and recycling. Our methodology incorporates a two-phase deep learning model, beginning with a U-Net architecture for detailed, pixel-level seg-mentation of waste materials. This phase achieves an Intersection over Union (IoU) of 0.902, ensuring precise material localization. Following this, a CNN based on EfficientNet is applied, achieving a classification accuracy of 99%, demonstrating significant improvement over traditional methods. Our comparative analysis highlights the practical advantages of this approach, including labor savings and superior classification accu-racy compared to manual sorting methods. By focusing on integrat-ing real-time processing capabilities, this research provides actionable insights for advancing automated CDW management systems. Addition-ally, providing codes, training and testing datasets, and supplementary resources facilitates further research and implementation. These find-ings underline the potential of machine learning to revolutionize CDW sorting, aligning with the principles of environmental sustainability and economic efficiency.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Futuristic Computational Systems and Advanced Engineering for the Society, Proceedings of the 6th International Conference on Artificial Intelligence and Applied Mathematics in Engineering ICAIAME 2024, Volume 2
ISBN
9783031946004
ISSN
2731-5010
e-ISSN
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Number of pages
17
Pages from-to
26-42
Publisher name
Springer Nature Switzerland AG
Place of publication
Basel
Event location
Warsaw
Event date
Sep 26, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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