[1] H CHO, H YOON, J JUNG. Image-based crack detection using crack width transform (CWT) algorithm. IEEE Access, 1, 60100-60114(2018).

[2] Y QUAN, J SUN, Y ZHANG et al. The method of the road surface crack detection by the improved otsu threshold, 1615-1620(2019).

[3] Z QU, Y CHEN, L LIU et al. The algorithm of concrete surface crack detection based on the genetic programming and percolation model. IEEE Access, 1, 57592-57603(2019).

[4] Suya ZHU, Jianchao DU, Yunsong LI et al. Bridge crack detection method using U-Net convolutional network. Journal of Xidian University, 46, 35-42(2019).

[5] Y ZHANG, B CHEN, J WANG et al. APLCNet: automatic pixel-level crack detection network based on instance segmentation. IEEE Access, 1, 199159-199170(2020).

[6] Z QU, J MEI, L LIU et al. Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model. IEEE Access, 1, 54564-54573(2020).

[7] Chunlin WU, Lingyan WU, Weibo GUO et al. Application of pavement crack detection based on tensor voting method. Instrumentation Technology, 36-37+40(2019).

[8] Congya WANG. Research on crack detection method of bridge bottom surface based on image recognition processing(2016).

[9] Lei WANG. Research on pavement crack classification and detection method based on machine vision(2019).

[10] A CUBERO-FERNANDEZ, F RODRIGUEZ-LAZANO, R VILLATORO et al. Efficient pavement crack detection and classification. EURASIP Journal on Image and Video Processing, 1-11(2017).

[11] Y J CHA, W CHOI. Deep learning-based crack damage detection using convolutional neural networks. Computer-aided Civil and Infrastructure Engineering, 32, 361-378(2017).

[12] H KIM, E AHN, M SHIN et al. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Monitoring, 18, 725-738(2019).

[13] G R LI, R Z LI, X SHEN et al. Crack and noncrack damage automatic classification from concrete surface images using broad network architecture, 1966-1971(2019).

[14] Xingbang PENG, Jianguo JIANG. An image threshold segmentation technology based on brightness equalization. Computer Technology and Development, 16, 10-12(2006).

[15] Dejin ZHANG, Qingquan LI, Ying CHEN et al. Asphalt pavement crack detection method based on spatial aggregation characteristics. Acta Automatica Sinica, 42, 443-454(2016).