[1] Wang W Q. Development and expectation of bridge engineering technology[J]. Construction Technology, 47, 103-108(2018).

[2] Qu L, Wang K R, Chen L L et al. Fast road detection based on RGBD images and convolutional neural network[J]. Acta Optica Sinica, 37, 1010003(2017).

[3] social development of China. China Information News,[N/OL]. 2018-02-28(003). https:∥baijiahao.baidu.com/s?id=1593656271082733578&wfr=spider&for=pc.(2017).

[4] Deng X L, Tian S Z. Bridge deformation detection and data processing based on 3D laser scanning[J]. Laser & Optoelectronics Progress, 55, 071201(2018).

[5] Wang B, Wang X, Chen F et al. Pavement crack recognition based on aerial image[J]. Acta Optica Sinica, 37, 0810004(2017).

[6] Oh H, Garrick N W, Achenie L. Segmentation algorithm using iterative clipping for processing noisy pavement images. [C]∥Imaging Technologies: 2nd International Conference on Techniques And Application In Civil Engineering, May 25-30 ,1997, Davos, Switzerland. Washington: TRB Publications, 138-147(1998).

[7] Sun L, Xing J C, Xie L Q et al. An adaptive threshold-based Canny algorithm for crack detection[J]. Microcomputer & Its Applications, 36, 35-37, 41(2017).

[8] Talab A M A, Huang Z C, Xi F et al. . Detection crack in image using Otsu method and multiple filtering in image processing techniques[J]. Optik, 127, 1030-1033(2016). http://www.sciencedirect.com/science/article/pii/S0030402615012164

[9] Zhang L, Yang F, Zhang Y D et al. Road crack detection using deep convolutional neural network. [C]∥IEEE International Conference on Image Processing, September 25-28,2016, Phoenix, AZ, USA. New York: IEEE, 3708-3712(2016).

[10] Chen F C, Jahanshahi M R. NB-CNN: Deep learning-based crack detection using convolutional neural network and naïve bayes data fusion[J]. IEEE Transactions on Industrial Electronics, 65, 4392-4400(2018). http://ieeexplore.ieee.org/document/8074762

[11] Shi Y, Cui L M, Qi Z Q et al. Automatic road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 17, 3434-3445(2016). http://ieeexplore.ieee.org/document/7471507/

[12] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. [C]∥International Conference on Learning Representations 2016 (ICLR 2016), May 2-4, 2016, San Juan, Puerto Rico. New York: Cornell University Library, 1511, 06434(2016).

[13] Klambauer G, Unterthiner T, Mayr A et al. Self-Normalizing neural networks. [C]∥Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. New York: Cornell University Library, 1706, 02515(2016).

[14] Li F X, Carreira J, Lebanon G et al. Composite statistical inference for semantic segmentation. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 3302-3309(2013).

[15] Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 55, 081005(2018).

[16] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017). http://arxiv.org/abs/1511.00561v3

[17] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://www.ncbi.nlm.nih.gov/pubmed/27244717/

[18] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018). http://europepmc.org/abstract/MED/28463186

[19] Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

[20] Jégou S, Drozdzal M, Vazquez D et al. The one hundred layers tiramisu: Fully convolutional DenseNets for semantic segmentation. [C]∥IEEE Conference on Computer Vision and Pattern Recognition Workshops, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1175-1183(2017).