Computer vision-assisted classification of recycled aggregates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00384552" target="_blank" >RIV/68407700:21110/25:00384552 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/B978-0-443-23962-5.00015-3" target="_blank" >https://doi.org/10.1016/B978-0-443-23962-5.00015-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/B978-0-443-23962-5.00015-3" target="_blank" >10.1016/B978-0-443-23962-5.00015-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computer vision-assisted classification of recycled aggregates

Popis výsledku v původním jazyce

Efficient construction and demolition waste (CDW) recycling or reuse are crucial for sustainable development, yet current practices often underutilize valuable materials. This chapter introduces an innovative machine-learning-based system for the localization and classification of CDW fragments, possibly enhancing industrial sorting efficiency and thus promoting environmental sustainability. Our approach involves high-resolution image acquisition, preprocessing, and advanced classification using U-Net and ResNet models. We provide a detailed methodology for training these models, focusing on feature extraction, data augmentation, and segmentation. The results demonstrate improved accuracy in identifying and sorting CDW materials, highlighting the potential for integration into existing recycling systems. By combining advanced algorithms with robust hardware, this technology can significantly increase the valorization rate of CDW, reduce waste, and support circular economy principles.

Název v anglickém jazyce

Computer vision-assisted classification of recycled aggregates

Popis výsledku anglicky

Efficient construction and demolition waste (CDW) recycling or reuse are crucial for sustainable development, yet current practices often underutilize valuable materials. This chapter introduces an innovative machine-learning-based system for the localization and classification of CDW fragments, possibly enhancing industrial sorting efficiency and thus promoting environmental sustainability. Our approach involves high-resolution image acquisition, preprocessing, and advanced classification using U-Net and ResNet models. We provide a detailed methodology for training these models, focusing on feature extraction, data augmentation, and segmentation. The results demonstrate improved accuracy in identifying and sorting CDW materials, highlighting the potential for integration into existing recycling systems. By combining advanced algorithms with robust hardware, this technology can significantly increase the valorization rate of CDW, reduce waste, and support circular economy principles.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Advances in Construction and Demolition Waste Recycling: Digital Technologies, Management, Processing and Environmental Assessment

ISBN

978-0-443-23962-5

Počet stran výsledku

24

Strana od-do

57-80

Počet stran knihy

471

Název nakladatele

Elsevier

Místo vydání

Kidlington Oxford OX GB

Kód UT WoS kapitoly

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