Abstract
Automatic crack detection is a main task in a crack map generation of the existing concrete infrastructure inspection. This paper presents an automatic crack detection and classification method based on genetic algorithm (GA) to optimize the parameters of image processing techniques (IPTs). The crack detection results of concrete infrastructure surface images under various complex photometric conditions still remain noise pixels. Next, a deep convolution neural network (DCNN) method is applied to classify crack candidates and non-crack candidates automatically. Moreover, the proposed method compared with the different deep learning methods for crack detection. The experimental results validate the reasonable accuracy in practical application.
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References
Fujita, Y., Mitani, Y., Hamamoto, Y.: A method for crack detection on a concrete structure. In: ICPR2006: IEEE 18th International Conference, on Pattern Recognition, vol. 3, New York, pp.901–904 (2006)
Nguyen, C., Kawamura, K., Tarighat, A.: A study on semi-automatic concrete cracks detection using interactive genetic algorithm. JCI 38(1), 2061–2066 (2016)
Kawamura, K., Yoshino, K., Nakamura, H., Sato, Tarighat, A.: A valid parameter range identification method of a digital image processing algorithm for concrete surface cracks detection using genetic algorithm and decision tree. Proc. Japan Soc. Civ. Eng. 69 (2013)
Zhang, W., Zhang, Z., Qi, D., Liu, Y.: Automatic crack detection and classification method for subway tunnel safety monitoring. Sensors 14, 19307–19328 (2014)
Nguyen, K., Kawamura, K., Tarighat, A.: Automatic Crack Detection for Concrete Infrastructures Using Image Processing and Deep Learning (2020). https://doi.org/10.1088/1757-899X/829/1/012027
Cha, Y., Choi, W.: Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aided Civ. Insfrastrure Eng. 32, 361–378 (2017)
Tong, Z., Gao, J., Sha, A., Hu, L.: Convolution neural network for asphalt pavement surface texture analysis. Comput.-Aided Civ. Infrastructure Eng. 0, 1–17 (2018)
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Nguyen, C.K., Kawamura, K., Nakamura, H. (2023). Deep Learning-Based Crack Detection and Classification for Concrete Structures Inspection. In: Geng, G., Qian, X., Poh, L.H., Pang, S.D. (eds) Proceedings of The 17th East Asian-Pacific Conference on Structural Engineering and Construction, 2022. Lecture Notes in Civil Engineering, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-7331-4_58
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DOI: https://doi.org/10.1007/978-981-19-7331-4_58
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