  We have developed an adaptive structural Deep Belief Network (Adaptive DBN)
that finds an optimal network structure in a self-organizing manner during
learning. The Adaptive DBN is the hierarchical architecture where each layer
employs Adaptive Restricted Boltzmann Machine (Adaptive RBM). The Adaptive RBM
can find the appropriate number of hidden neurons during learning. The proposed
method was applied to a concrete image benchmark data set SDNET2018 for crack
detection. The dataset contains about 56,000 crack images for three types of
concrete structures: bridge decks, walls, and paved roads. The fine-tuning
method of the Adaptive DBN can show 99.7%, 99.7%, and 99.4% classification
accuracy for three types of structures. However, we found the database included
some wrong annotated data which cannot be judged from images by human experts.
This paper discusses consideration that purses the major factor for the wrong
cases and the removal of the adversarial examples from the dataset.

Computer Vision and Pattern Recognition;Neural and Evolutionary Computing