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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/SS03010302" target="_blank" >SS03010302: Vývoj efektivních nástrojů pro minimalizaci vzniku stavebního a demoličního odpadu, jeho monitoring a opětovné využití</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

238

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

121568-121581

Kód UT WoS článku

001092704600001

EID výsledku v databázi Scopus

2-s2.0-85172441953