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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References
Koch, C., et al.: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics 29(2), 196–210 (2015)
Mohammad, R., et al.: A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures. Structure and Infrastructure Engineering 5(6), 455–486 (2009)
Abdelqader, I., et al.: Analysis of edge-detection techniques for crack identification in bridges. Journal of Computing in Civil Engineering 17(4), 255–263 (2015)
Yamaguchi, T., et al.: Image-based crack detection for real concrete surfaces. IEEE Transactions on Electrical & Electronic Engineering 3(1), 128–135 (2010)
Oliveira, H., Lobato Correia, P.: Automatic road crack segmentation using entropy and image dynamic thresholding. In: 17th European Signal Processing Conference, pp. 622–626. IEEE (2009)
Hu, D., et al.: Wall crack detection based on image processing. In: 3rd International Conference on Intelligent Control and Information Processing, pp. 597–600. IEEE (2012)
Yamaguchi, T., Nakamura, S., Hashimoto, S.: An efficient crack detection method using percolation-based image processing. In: 3rd IEEE Conference on Industrial Electronics and Applications, pp. 1875–1880. IEEE (2008)
Yamaguchi, T., Hashimoto, S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Machine Vision & Applications 21(5), 797–809 (2010)
Abdel-Qader, I., et al.: PCA-based algorithm for unsupervised bridge crack detection. Advances in Engineering Software 37(12), 771–778 (2006)
Hutchinson, T., et al.: Improved image analysis for evaluating concrete damage. Journal of Computing in Civil Engineering 20(3), 210–216 (2006)
Cui, F., et al.: Images crack detection technology based on improved K-means algorithm. Journal of Multimedia 9(6), 67–73 (2014)
Moon, H., Kim, J., et al.: Inteligent crack detecting algorithm on the concrete crack image using neural network. In: International Symposium on Automation and Robotics in Construction, (2011). 10.22260/ISARC2011/0279
Cha, Y., et al.: Deep learning-based crack damage detection using convolutional neural networks. Computer-aided Civil & Infrastructure Engineering 32(5), 361–378 (2017)
Hao, M., et al.: An improved neuron segmentation model for crack detection C Image Segmentation Model. Cybernetics & Information Technologies 17(2), 119–133 (2017)
Ronneberger, O., et al.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-net. IEEE Geoscience and Remote Sensing Letters 15(5), 749–753 (2018)
Vladimir, I., et al.: Satellite imagery feature detection using deep convolutional neural network: a Kaggle competition. ArXiv:1706.06169 CS. (2017)
Gao, X., et al.: Retinal blood vessel segmentation based on the Gaussian matched filter and U-net. In: 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. IEEE (2017)
He, K., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision, pp. 1026–1034. (2015)
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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