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A Decision System of Crack Damage Rank on Infrastructures Using Deep Learning
Description
The ageing of infrastructures in Japan is progressing. Along with that, there is a shortage of experts who inspect the structures. Therefore, the support system of inspection work for structures has been demanded. So, in our laboratory, development of the damage rank decision system for structures has been developed. In this paper, we try to decide the damage rank for structures with Deep Learning, one of machine learning which attracts attention in the field of image identification in recent years. There are several types of structural damage. Firstly, we try to make models for cracks. There are about 200 images and numerical information as damage data(training and test data). In construction of the CNN models, we tried 4 input data patterns. As the results, the entire accuracy was 54.6[%] at 300x300, 57.29% at 600x600. In case of accuracy for each rank, we achieved that the best accuracy was 80.95% at rank 2 decision.
Journal
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- Proceedings of The 6th IIAE International Conference on Intelligent Systems and Image Processing 2018
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Proceedings of The 6th IIAE International Conference on Intelligent Systems and Image Processing 2018 76-82, 2018-01-01
The Institute of Industrial Application Engineers
