Abstract
Cracks are the most common and important diseases of concrete bridges. A dual convolutional neural network (DCN) model which is composed of one convolutional neural network (CNN) model and one fully convolutional network (FCN) model is proposed to recognize the cracks in image. Firstly, the CNN model is used to identify the crack area. The interfering factors such as spot, shadow, water stain, and graffiti in the non-crack area will be excluded by CNN model. Then, the CNN results will be segmented by the FCN model with the structure of merging layer-by-layer to extract crack features such as length and width. The DCN model is trained to recognize the actual concrete bridge cracks in this paper. The recognition results show that the DCN model has a good balance between high accuracy and low noise in the process of crack recognition compared with the current image recognition method. The reliability and accuracy of recognition are both greatly improved. The DCN model is helpful for automatic identification of cracks in concrete bridges.
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Abdel-Hamid, O., Mohamed, A. R., Jiang, H., Deng, L., Penn, G., and Yu, D. (2014). “Convolutional neural networks for speech recognition.” IEEE/ACM Transactions on Audio Speech & Language Processing, Vol. 22, No. 10, pp. 1533–1545, DOI: https://doi.org/10.1109/TASLP.2014.2339736.
Abdelqader, I., Abudayyeh, O., and Kelly, M. E. (2003). “Analysis of edge-detection techniques for crack identification in bridges.” Journal of Computing in Civil Engineering, Vol. 17, No. 4, pp. 255–263, DOI: https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255).
Cha, Y. J., Choi, W., and Büyüköztürk, O. (2017). “Deep learning-based crack damage detection using convolutional neural networks.” Computer-aided Civil & Infrastructure Engineering, Vol. 32, No. 5, pp. 361–378, DOI: https://doi.org/10.1111/mice.12263.
Fujita, Y., Mitani, Y., and Hamamoto, Y. (2006). “A method for crack detection on a concrete structure.” International Conference on Pattern Recognition, Vol. 3, pp. 901–904, DOI: https://doi.org/10.1109/ICPR.2006.98.
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proc., 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Columbus, OH, USA, pp. 580–587, DOI: https://doi.org/10.1109/CVPR.2014.81.
Halfawy, M. R. and Hengmeechai, J. (2014). “Integrated vision-based system for automated defect detection in sewer closed circuit television inspection videos.” Journal of Computing in Civil Engineering, Vol. 29, No. 1, p. 04014024, DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000312.
Hinton, G. E. and Salakhutdinov, R. R. (2006). “Reducing the dimensionality of data with neural networks.” Science, Vol. 313, No. 5786, pp. 504–507, DOI: https://doi.org/10.1126/science.1127647.
Kaseko, M., Lo, Z., and Ritchie, S. (1994). “Comparison of traditional and neural classifiers for pavement-crack detection.” Journal of Transportation Engineering, Vol. 120, No. 4, pp. 552–569, DOI: https://doi.org/10.1061/(ASCE)0733-947X(1994)120:4(552).
Lecun, Y., Bengio, Y., and Hinton, G. (2015). “Deep learning.” Nature, Vol. 521, pp. 436–444, DOI: https://doi.org/10.1038/nature14539.
Lee, J. H., Lee, J. M., Kim, H. J., and Moon, Y. S. (2008). “Machine vision system for automatic inspection of bridges.” Congress on Image & Signal Processing, Vol. 3, pp. 363–366, DOI: https://doi.org/10.1109/CISP.2008.672.
Li, Y. D., Hao, Z., and Lei, H. (2016). “Survey of convolutional neural network.” Journal of Computer Applications, Vol. 36, No. 9, pp. 2508–2515, DOI: https://doi.org/10.11772/j.issn.1001-9081.2016.09.2508.
Long, J., Shelhamer, E., and Darrell, T. (2015). “Fully convolutional networks for semantic segmentation.” Proc., 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 3431–3440, DOI: https://doi.org/10.1109/CVPR.2015.7298965.
Lu, H. and Zhang, Q. (2016). “Applications of deep convolutional neural network in computer vision.” Journal of Data Acquisition & Processing, Vol. 31, No. 1, pp. 1–17, DOI: https://doi.org/10.16337/j.1004-9037.2016.01.001.
Oh, J. K., Jang, G., Oh, S., Lee, J. H., Yi, B. J., Moon, Y. S., Lee, J. S. and Choi, Y. (2009). “Bridge inspection robot system with machine vision.” Automation in Construction, Vol. 18, No. 7, pp. 929–941, DOI: https://doi.org/10.1016/j.autcon.2009.04.003.
Scherer, D., Müller, A., and Behnke, S. (2010). “Evaluation of pooling operations in convolutional architectures for object recognition.” Artificial Neural Networks-ICANN 2010, Springer, Berlin, Heidelberg, Vol. 6354, pp. 92–101, DOI: https://doi.org/10.1007/978-3-642-15825-4_10.
Sha, A. M., Tong, Z., Gao, J., Highway, S. O., and University, C. (2018). “Recognition and measurement of pavement disasters based on convolutional neural networks.” China Journal of Highway & Transport, Vol. 31, No. 1, pp. 1–10, DOI: https://doi.org/10.19721/j.cnki.1001-7372.2018.01.001.
Wang, P., Huang, H., and Xue, Y. (2012). “Automatic recognition of cracks in tunnel lining based on characteristics of local grids in images.” Chinese Journal of Rock Mechanics & Engineering, Vol. 31, No. 5, pp. 991–999, DOI: https://doi.org/10.3969/j.issn.1000-6915.2012.05.016.
Xavier, G., Antoine, B., and Yoshua, B. (2011). “Deep sparse rectifier neural networks.” Proc. 14th International Conference on Artificial Intelligence and Statistics, Lauderdale, FL, USA, Vol. 15, pp. 315–323, DOI: 10.1.1.208.6449.
Yamaguchi, T., Nakamura, S., and Hashimoto, S. (2008). “An efficient crack detection method using percolation-based image processing.” Proc., 2008 3rd IEEE Conference on Industrial Electronics and Applications, Singapore, pp. 1875–1880, DOI: https://doi.org/10.1109/ICIEA.2008.4582845.
Yu, S. N., Jang, J. H., and Han, C. S. (2007). “Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel.” Automation in Construction, Vol. 16, No. 3, pp. 255–261, DOI:https://doi.org/10.1016/j.autcon.2006.05.003.
Zhang, D. F. (2014). Image-based crack detection and properties retrieval for railway concrete bridge, MSc Thesis, Beijing Jiaotong University, Beijing, China.
Zhao, S. S. and Ning, H. E. (2017). “Road surface crack detection based on CNN.” Transducer & Microsystem Technologies, Vol. 36, No. 11, pp. 135–138, DOI: https://doi.org/10.13873/J.1000-9787(2017)11-0135-04.
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Acknowledgement is given to Tianjin Transportation Committee for supporting the research program mentioned in this paper.
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Liang, D., Zhou, XF., Wang, S. et al. Research on Concrete Cracks Recognition based on Dual Convolutional Neural Network. KSCE J Civ Eng 23, 3066–3074 (2019). https://doi.org/10.1007/s12205-019-2030-x
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DOI: https://doi.org/10.1007/s12205-019-2030-x