Automated Crack Severity Level Detection and Classification for Surface Crack Using Deep Convolutional Neural Networks
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-2980-9_21
Reference32 articles.
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3. Liu, P., Lim, H., Yang, S., Sohn, H.: Development of a “stick-and-detect’’ wireless sensor node for fatigue crack detection. Struct. Health Monit. 16, 153–163 (2016)
4. Zhao, S., Sun, L., Gao, J., Wang, J.: Uniaxial ACFM detection system for metal crack size estimation using magnetic signature waveform analysis. Measurement 164, 108090 (2020)
5. Yang, X., Zhou, Z.: Design of crack detection system. In: Proceedings of the 2017 International Conference on Network and Information Systems for Computers, Shanghai, China, 14–16 April 2017
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