[1] Li Q Q, Liu X L. Novel approach to pavement image segmentation based on neighboring difference histogram method[C]. //2008 Congress on Image and Signal Processing, May 27-30, 2008, Sanya, Hainan, China., 792-796(2008).

[2] Li Q Q, Zou Q, Zhang D Q et al. FoSA: F* seed-growing approach for crack-line detection from pavement images[J]. Image and Vision Computing, 29, 861-872(2011).

[3] Arbeláez P, Maire M, Fowlkes C et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011). http://www.mendeley.com/research/community-detection-hierarchical-image-segmentation-4/

[4] Zhao H L, Qin G F, Wang X J. Improvement of canny algorithm based on pavement edge detection[C]//2010 3rd International Congress on Image and Signal Processing, October 16-18, 2010, Yantai, China., 964-967(2010).

[5] Amhaz R, Chambon S, Idier J et al. Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection[J]. IEEE Transactions on Intelligent Transportation Systems, 17, 2718-2729(2016). http://dl.acm.org/citation.cfm?id=3084725

[6] Subirats P, Dumoulin J, Legeay V et al. Automation of pavement surface crack detection using the continuous wavelet transform[C]//2006 International Conference on Image Processing, October 8-11, 2006, Atlanta, GA, USA., 3037-3040(2006).

[7] Oliveira H, Correia P L. Automatic road crack detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 14, 155-168(2013).

[8] 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).

[9] Qu Z, Bai L, An S Q et al. Lining seam elimination algorithm and surface crack detection in concrete tunnel lining[J]. Journal of Electronic Imaging, 25, 063004(2016). http://proceedings.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-25/issue-6/063004/Lining-seam-elimination-algorithm-and-surface-crack-detection-in-concrete/10.1117/1.JEI.25.6.063004.full

[10] Yang X C, Li H, Yu Y T et al. Automatic pixel-level crack detection and measurement using fully convolutional network[J]. Computer-Aided Civil and Infrastructure Engineering, 33, 1090-1109(2018). http://onlinelibrary.wiley.com/doi/10.1111/mice.12412

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

[12] Xu W, Tang Z M, Zhou J et al. Pavement crack detection based on saliency and statistical features[C]//2013 IEEE International Conference on Image Processing, September 15-18, 2013, Melbourne, VIC, Australia., 4093-4097(2013).

[13] 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).

[14] Zou Q, Zhang Z, Li Q Q et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 28, 1498-1512(2019).

[15] Lee C, Xie S, Gallagher P W et al. Deeply-supervised nets[EB/OL]. (2014-09-25)[2020-07-10]. https://arxiv.org/abs/1409.5185v2.

[16] Perazzi F, Krähenbühl P, Pritch Y et al. Saliency filters: contrast based filtering for salient region detection[C]. //2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA, 733-740(2012).

[17] Zhu W J, Liang S, Wei Y C et al. Saliency optimization from robust background detection[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 2814-2821(2014).

[18] Cheng M M, Zhang G X, Mitra N J et al. Global contrast based salient region detection[C]//CVPR 2011, June 20-25, 2011, Providence, RI, USA., 409-416(2011).

[19] 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).