A Review of the Application of CNN-Based Computer Vision in Civil Infrastructure Maintenance

Ruying Cai, Jingru Li, Geng Li, Dongdong Tang and Yi Tan ()
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Ruying Cai: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Jingru Li: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Geng Li: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Dongdong Tang: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University
Yi Tan: Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University

A chapter in Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate, 2021, pp 643-659 from Springer

Abstract: Abstract Computer-vision and deep-learning techniques are being increasingly applied to the maintenance of civil infrastructure, such as inspecting, monitoring, and assessing infrastructure conditions, which overcome time-consuming and laborious compared with traditional technology. In this paper, the research progress of deep learning, the developments of convolutional neural network (CNN)-based computer vision in improving accuracy, reliability and generalized object detection capability and its application in civil infrastructure maintenance are reviewed. The main objectives are as follows: (1) clarify the application of deep learning in computer vision to help researchers systematically understand deep learning; (2) review the application of computer vision in civil infrastructure maintenance to help researchers pay more attention to its advantages; (3) encourage relevant personnel to use this research as a reference, take deep learning as an important method at the forefront of engineering management, generate more innovations in the construction field, and promote the development of the construction industry.

Keywords: Civil infrastructure; Computer vision; Convolutional neural networks; Deep learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-3587-8_42

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DOI: 10.1007/978-981-16-3587-8_42

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