Abstract
Crack detection is significant for the inspection and diagnosis of concrete structures. Various automated approaches have been developed to replace human-conducted inspection, many of which are not adaptive to various conditions and unable to provide localization information. In this paper, an end-to-end semantic segmentation neural network based on U-net is employed to detect crack. Due to the limited number of available annotated samples, data augmentation is employed to avoid overfitting. The adopted network is trained by only 200 images of 512 \(\times \) 512 pixels resolutions and achieves a satisfactory accuracy of 99.56% after 37 epochs. The output is an image of the same size as the input image where each pixel is assigned a class label, i.e. crack or not crack. It takes about 7 s to process an image of designed size on CPU. Combined with sliding window technique, our model can cope with any image of larger size. Comparative experiment results show that our model outperforms traditional Canny and Sobel edge detection methods in a variety of complex environment without extracting features manually.
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Acknowledgements
The authors would like to acknowledge the supports by the National Natural Science Foundation of China (Grant No. 61601127, 51508105, and 61574038), the Fujian Provincial Department of Science and Technology of China (Grant No. 2016H6012, and 2018J0106), the Fujian Provincial Economic and Information Technology Commission of China (Grant No. 830020, 83016006), and the Science Foundation of Fujian Education Department of China (Grant No. JAT160073).
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Ji, J., Wu, L., Chen, Z., Yu, J., Lin, P., Cheng, S. (2018). Automated Pixel-Level Surface Crack Detection Using U-Net. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_6
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DOI: https://doi.org/10.1007/978-3-030-03014-8_6
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