The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion
Abstract
Regular inspection of nuclear power plant components is important to guarantee safe operations. However, current practice is time consuming, tedious, and subjective, which involves human technicians reviewing the inspection videos and identifying cracks on reactors. A few vision-based crack detection approaches have been developed for metallic surfaces, and they typically perform poorly when used for analyzing nuclear inspection videos. Detecting these cracks is a challenging task since they are tiny, and noisy patterns exist on the components' surfaces. This study proposes a deep learning framework, based on a convolutional neural network (CNN) and a Naïve Bayes data fusion scheme, called NB-CNN, to analyze individual video frames for crack detection while a novel data fusion scheme is proposed to aggregate the information extracted from each video frame to enhance the overall performance and robustness of the system. To this end, a CNN is proposed to detect crack patches in each video frame, while the proposed data fusion scheme maintains the spatiotemporal coherence of cracks in videos, and the Naïve Bayes decision making discards false positives effectively. The proposed framework achieves a 98.3% hit rate against 0.1 false positives per frame that is significantly higher than state-of-the-art approaches as presented in this paper.
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Cited by
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Autonomous concrete crack detection using deep fully convolutional neural network
Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170
Dorafshan, S., Thomas, R. J., & Maguire, M. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.08.011 · Cited by 774
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Metadata
- Authors
- Chen, F.-C., & Jahanshahi, M. R.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2018-05
- Volume
- 65
- Issue
- 5
- Pages
- 4392-4400
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2765854388
- Cited by
- 930
- References
- 14
- FWCI
- 80.07
- Type
- journal article
- Language
- en
- Open access
- closed
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