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Convolutional Neural Network and Naive Bayes Date Fusion for Deep Learning-based Crack Detection

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 review the inspection videos and identify 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 called NBCNN 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 Convolutional Neural Network (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 naive Bayes decision making discards false positives effectively. The proposed framework achieves 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 study.

Related Publication

Fu-Chen Chen and Mohammad R. Jahanshahi, (2018), "NB-CNN: Deep learning-based crack detection using convolutional neural network and naive Bayes data fusion," IEEE Transactions on Industrial Electronics, Vol. 65, No. 5, 4392-4400.