[1] SONG Q, LIN G Y, MA J Q et al. An edge-detection method based on adaptive canny algorithm and iterative segmentation threshold[C], 64-67(2016).

[2] LI Q Q, LIU X L. Novel approach to pavement image segmentation based on neighboring difference histogram method[C], 792-796(2008).

[3] WU S G, LIU Y K. A segment algorithm for crack dection[C], 674-677(2012).

[4] 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).

[5] 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).

[6] LAU S L H, CHONG E K P, YANG X et al. Automated Pavement Crack Segmentation Using U-Net-Based Convolutional Neural Network[webpage]. arXiv, 2001-01912(2020). http：//arxiv.org/abs/2001.01912.pdf

[7] [7] 李海丰， 景攀， 韩红阳. 基于可变形卷积与特征融合的机场道面裂缝检测算法［J］. 南京航空航天大学学报， 2021， 53（6）： 981-988. doi: 10.16356/j.1005-2615.2021.06.018LIH F， JINGP， HANH Y. Airport pavement crack detection algorithm based on deformable convolution and feature fusion［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2021， 53（6）： 981-988.（in Chinese）. doi: 10.16356/j.1005-2615.2021.06.018

[8] [8] 徐胜军， 郝明， 孟月波， 等. 基于特征增强整体嵌套网络裂缝检测方法［J］. 激光与光电子学进展， 2022， 59（10）： 1010003. doi: 10.3788/LOP202259.1010003XUS J， HAOM， MENGY B， et al. Crack detection method of holistically-nested network based on feature enhancement［J］. Laser & Optoelectronics Progress， 2022， 59（10）： 1010003.（in Chinese）. doi: 10.3788/LOP202259.1010003

[9] XU Z S, GUAN H Y, KANG J et al. Pavement crack detection from CCD images with a locally enhanced transformer network[J]. International Journal of Applied Earth Observation and Geoinformation, 110, 102825(2022).

[10] JI W P, ZHANG Y Z, HUANG P F et al. A neural network with spatial attention for pixel-level crack detection on concrete bridges[C], 481-486(2022).

[11] [11] 李良福， 王楠， 武彪， 等. 基于改进PSPNet的桥梁裂缝图像分割算法［J］. 激光与光电子学进展， 2021， 58（22）： 2210001. doi: 10.3788/LOP202158.2210001LIL F， WANGN， WUB， et al. Segmentation algorithm of bridge crack image based on modified PSPNet［J］. Laser & Optoelectronics Progress， 2021， 58（22）： 2210001.（in Chinese）. doi: 10.3788/LOP202158.2210001

[12] [12] 祝一帆， 王海涛， 李可， 等. 一种高精度路面裂缝检测网络结构：Crack U-Net［J］. 计算机科学， 2022， 49（1）： 204-211. doi: 10.11896/jsjkx.210100128ZHUY F， WANGH T， LIK， et al. Crack U-net： towards high quality pavement crack detection［J］. Computer Science， 2022， 49（1）： 204-211.（in Chinese）. doi: 10.11896/jsjkx.210100128

[13] [13] 瞿中， 陈雯. 基于空洞卷积和多特征融合的混凝土路面裂缝检测［J］. 计算机科学， 2022， 49（3）： 192-196. doi: 10.11896/jsjkx.210100164QUZ， CHENW. Concrete pavement crack detection based on dilated convolution and multi-features fusion［J］. Computer Science， 2022， 49（3）： 192-196.（in Chinese）. doi: 10.11896/jsjkx.210100164

[14] QU Z, CHEN W, WANG S Y et al. A crack detection algorithm for concrete pavement based on attention mechanism and multi-features fusion[J]. IEEE Transactions on Intelligent Transportation Systems, 23, 11710-11719(2022).

[15] QU Z, WANG C Y, WANG S Y et al. A method of hierarchical feature fusion and connected attention architecture for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 23, 16038-16047(2022).

[16] QU Z, CAO C, LIU L et al. A deeply supervised convolutional neural network for pavement crack detection with multiscale feature fusion[J]. IEEE Transactions on Neural Networks and Learning Systems, 33, 4890-4899(2022).

[17] HOU Q B, ZHANG L, CHENG M M et al. Strip pooling： rethinking spatial pooling for scene parsing[C], 4002-4011(2020).

[18] YANG F, ZHANG L, YU S J et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 21, 1525-1535(2020).

[19] LIU Y H, YAO J, LU X H et al. DeepCrack： a deep hierarchical feature learning architecture for crack segmentation[J]. Neurocomputing, 338, 139-153(2019).

[20] ZHOU Q, QU Z, JU F R. A lightweight network for crack detection with split exchange convolution and multi-scale features fusion[J]. IEEE Transactions on Intelligent Vehicles, 8, 2296-2306(2023).

[21] LIU C Q, ZHU C G, XIA X et al. FFEDN： feature fusion encoder decoder network for crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 23, 15546-15557(2022).

[22] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[C], 640-651(2017).

[23] 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).

[24] 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).

[25] ZHAO H S, SHI J P, QI X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[26] ZOU Q, ZHANG Z, LI Q Q et al. DeepCrack： learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 1498-1512(2018).

[27] SUN K, ZHAO Y, JIANG B R et al. High-Resolution Representations for Labeling Pixels and Regions[webpage]. arXiv, 1904-04514(2019). http：//arxiv.org/abs/1904.04514.pdf

[28] RONNEBERGER O, FISCHER P, BROX T. U-net： convolutional networks for biomedical image segmentation[M]. Lecture Notes in Computer Science, 234-241(2015).