Deep Learning Approaches for Crack Detection in Bridge Concrete Structures

Daniel Einarson; Dawit Mengistu

doi:10.1109/ICESIC53714.2022.9783576

Deep Learning Approaches for Crack Detection in Bridge Concrete Structures

Daniel Einarson, Dawit Mengistu

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Kapitel i bok › Peer review

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Deep Learning Approaches for Crack Detection in Bridge Concrete Structure

Originalspråk	Engelska
Titel på värdpublikation	Proceedings of the 2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022
Förlag	Institute of Electrical and Electronics Engineers (IEEE)
Sidor	7-12
ISBN (elektroniskt)	978-1-6654-8385-8
ISBN (tryckt)	9781665483858
DOI	https://doi.org/10.1109/ICESIC53714.2022.9783576
Status	Publicerad - 2022
Evenemang	International Conference on Electronic Systems and Intelligent Computing - Chennai, Indien Varaktighet: 2022-apr.-22 → 2022-apr.-23

Konferens

Konferens	International Conference on Electronic Systems and Intelligent Computing
Förkortad titel	ICESIC
Land/Territorium	Indien
Ort	Chennai
Period	22-04-22 → 22-04-23

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Datavetenskap (10201)

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10.1109/ICESIC53714.2022.9783576

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title = "Deep Learning Approaches for Crack Detection in Bridge Concrete Structures",

abstract = "Convolutional Neural Networks are among the most effective algorithms for image analysis applications. However, the accuracy of the algorithms depends on the availability of powerful computational resources and the quality of the images used to train the models. This paper investigates ways to build robust models to detect cracks in concrete structures using low resolution images and third-party datasets. Our experiments show that reducing image sizes by a factor of 4 does not significantly impact the accuracy. This is helpful to shorten execution time and hence lower cloud service costs. It is also observed that applying a model trained on one image dataset to detect cracks in images from a different source is not a trivial task.",

author = "Daniel Einarson and Dawit Mengistu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; International Conference on Electronic Systems and Intelligent Computing, ICESIC ; Conference date: 22-04-2022 Through 23-04-2022",

year = "2022",

doi = "10.1109/ICESIC53714.2022.9783576",

language = "English",

isbn = "9781665483858",

pages = "7--12",

booktitle = "Proceedings of the 2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

Einarson, D & Mengistu, D 2022, Deep Learning Approaches for Crack Detection in Bridge Concrete Structures. i Proceedings of the 2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022. Institute of Electrical and Electronics Engineers (IEEE), s. 7-12, International Conference on Electronic Systems and Intelligent Computing, Chennai, Indien, 22-04-22. https://doi.org/10.1109/ICESIC53714.2022.9783576

Deep Learning Approaches for Crack Detection in Bridge Concrete Structures. / Einarson, Daniel ; Mengistu, Dawit.

Proceedings of the 2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022. Institute of Electrical and Electronics Engineers (IEEE), 2022. s. 7-12.

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Kapitel i bok › Peer review

TY - CHAP

T1 - Deep Learning Approaches for Crack Detection in Bridge Concrete Structures

AU - Einarson, Daniel

AU - Mengistu, Dawit

PY - 2022

Y1 - 2022

N2 - Convolutional Neural Networks are among the most effective algorithms for image analysis applications. However, the accuracy of the algorithms depends on the availability of powerful computational resources and the quality of the images used to train the models. This paper investigates ways to build robust models to detect cracks in concrete structures using low resolution images and third-party datasets. Our experiments show that reducing image sizes by a factor of 4 does not significantly impact the accuracy. This is helpful to shorten execution time and hence lower cloud service costs. It is also observed that applying a model trained on one image dataset to detect cracks in images from a different source is not a trivial task.

AB - Convolutional Neural Networks are among the most effective algorithms for image analysis applications. However, the accuracy of the algorithms depends on the availability of powerful computational resources and the quality of the images used to train the models. This paper investigates ways to build robust models to detect cracks in concrete structures using low resolution images and third-party datasets. Our experiments show that reducing image sizes by a factor of 4 does not significantly impact the accuracy. This is helpful to shorten execution time and hence lower cloud service costs. It is also observed that applying a model trained on one image dataset to detect cracks in images from a different source is not a trivial task.

U2 - 10.1109/ICESIC53714.2022.9783576

DO - 10.1109/ICESIC53714.2022.9783576

M3 - Chapter in book

SN - 9781665483858

SP - 7

EP - 12

BT - Proceedings of the 2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - International Conference on Electronic Systems and Intelligent Computing

Y2 - 22 April 2022 through 23 April 2022

ER -

Deep Learning Approaches for Crack Detection in Bridge Concrete Structures

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