NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

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 detectio...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 65; no. 5; pp. 4392 - 4400
Main Authors: Chen, Fu-Chen, Jahanshahi, Mohammad R.
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Bayesian analysis
Cameras
convolutional neural network (CNN)
Crack detection
Cracks
data fusion
Data integration
Decision making
Deep learning
Electric power plants
Inspection
Military strategy
Multisensor fusion
Neural networks
Nondestructive testing
Nuclear electric power generation
Nuclear fusion
nuclear power plant inspection
Nuclear power plants
Nuclear reactors
Power generation
Power plant components
Surface cracks
Surface morphology
Videos
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary: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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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2764844