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A novel approach fo...
A novel approach for industrial concrete defect identification based on image processing and deep convolutional neural networks
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- Gaur, Ashish (författare)
- Department of Biotechnology, GLA University, Uttar Pradesh, Mathura, India
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- Kishore, Kamal (författare)
- School of Civil, Mining and Environmental Engineering, University of Wollongong, Australia; Department of Civil Engineering, GLA University, Uttar Pradesh, Mathura, India
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- Jain, Rajul (författare)
- Department of Zoology, Dayalbagh Educational Institute, Uttar Pradesh, Agra, India
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- Pandey, Aaysha (författare)
- Department of Chemistry, GLA University, Uttar Pradesh, Mathura, India
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- Singh, Prakash (författare)
- Department of Civil Engineering, Shiv Nadar Institution of Eminence, Gautam Buddha Nagar, Uttar Pradesh, India
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- Kumar Wagri, Naresh (författare)
- Umeå universitet,Institutionen för tillämpad fysik och elektronik
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- Roy-Chowdhury, Abhirup B. (författare)
- WSP Research and Innovation, Lower Hutt, New Zealand
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(creator_code:org_t)
- Elsevier, 2023
- 2023
- Engelska.
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 19
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The preservation of structural integrity and durability is essential for the long-term viability of civil infrastructure projects. Addressing concrete defects promptly is crucial to achieving this objective. In this research, the research proposes a novel method for concrete defect analysis, harnessing the potential of image processing and deep learning techniques. It employs a fusion-based deep convolutional neural network (CNN), amalgamating the features of Inception V3, VGG16, and AlexNet architectures to identify and classify six distinct concrete defect characteristics, namely Cracks, Crazing, Efflorescence, Pop-out, Scaling, and Surface Cracks. Through rigorous training and validation, we thoroughly assess the performance of the proposed fusion-based CNN model. The testing phase reveals precision rates, with Crazing showing the lowest precision at 95%, and Cracks/Pop-outs achieving 98%, while other defect classifications exhibit exceptional 100% precision rates. The overall efficacy of our proposed model is evaluated using accuracy and F1-score metrics. Our findings demonstrate an impressive overall accuracy of 98.31% and an F1-score of 0.98, affirming the robustness and reliability of our approach. The outcomes of this study signify a significant advancement toward accurate and automated detection and classification of concrete defects.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
Nyckelord
- Building
- Classification
- Crack
- Deep CNN
- Fusion
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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