Influence of image noise on crack detection performance of deep convolutional neural networks

Chianese, R. and Nguyen, A. ORCID: https://orcid.org/0000-0001-8739-8207 and Gharehbaghi, V. R. and ‪Aravinthan‬, T. ORCID: https://orcid.org/0000-0003-0691-8296 and Noori, M. (2021) Influence of image noise on crack detection performance of deep convolutional neural networks. In: 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10), 30 June - 2 July 2021, Porto, Portugal.

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Abstract

Development of deep learning techniques to analyse image data is an expansive and emerging field. The benefits of tracking, identifying, measuring, and sorting features of interest from image data has endless applications for saving cost, time, and improving safety. Much research has been conducted on classifying cracks from image data using deep convolutional neural networks; however, minimal research has been conducted to study the efficacy of network performance when noisy images are used. This paper will address the problem and is dedicated to investigating the influence of image noise on network accuracy. The methods used incorporate a benchmark image data set, which is purposely deteriorated with two types of noise, followed by treatment with image enhancement pre-processing techniques. These images, including their native counterparts, are then used to train and validate two different networks to study the differences in accuracy and performance. Results from this research reveal that noisy images have a moderate to high impact on the network's capability to accurately classify images despite the application of image pre-processing. A new index has been developed for finding the most efficient method for classification in terms of computation timing and accuracy. Consequently, AlexNet was selected as the most efficient model based on the proposed index


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 16 Feb 2022 03:21
Last Modified: 16 Mar 2022 03:28
Uncontrolled Keywords: crack detection; transfer learning; deep convolution neural network; image noise; network performance
Fields of Research (2008): 09 Engineering > 0905 Civil Engineering > 090506 Structural Engineering
Fields of Research (2020): 40 ENGINEERING > 4005 Civil engineering > 400508 Infrastructure engineering and asset management
URI: http://eprints.usq.edu.au/id/eprint/45455

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