Machine-learning-assisted classification of construction and demolition waste fragments using computer vision: Convolution versus extraction of selected features

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00368863" target="_blank" >RIV/68407700:21110/24:00368863 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.eswa.2023.121568" target="_blank" >https://doi.org/10.1016/j.eswa.2023.121568</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2023.121568" target="_blank" >10.1016/j.eswa.2023.121568</a>

Result language

angličtina

Original language name

Machine-learning-assisted classification of construction and demolition waste fragments using computer vision: Convolution versus extraction of selected features

Original language description

Improper sorting of construction and demolition waste (CDW) leads to significant environmental and economic implications, including inefficient resource use and missed recycling opportunities. To address this, we developed a machine-learning-assisted procedure for recognizing CDW fragments using an RGB camera. Our approach uniquely leverages selected feature extraction, enhancing classification speed and accuracy. We employed three classifiers: convolutional neural network (CNN), gradient boosting (GB) decision trees, and multi-layer perception (MLP). Notably, our method's extraction of selected features for GB and MLP outperformed the traditional CNN in terms of speed and accuracy, especially for challenging samples with similar textures. Specifically, while convolution resulted in an overall accuracy of 85.9%, our innovative feature extraction approach yielded accuracies up to 92.3%. This study's findings have significant implications for the future of CDW management, offering a pathway for efficient and accurate waste sorting, fostering sustainable resource use, and reducing the environmental impact of CDW disposal. Supplementary materials, including datasets, codes, and models, are provided, promoting transparency and reproducibility.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

<a href="/en/project/SS03010302" target="_blank" >SS03010302: Development of efficient tools to minimize production of construction and demolition waste, its monitoring and reuse</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Volume of the periodical

238

Issue of the periodical within the volume

March

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

121568-121581

UT code for WoS article

001092704600001

EID of the result in the Scopus database

2-s2.0-85172441953