[1] Dong C Z, Catbas F N. A review of computer vision-based structural health monitoring at local and global levels[J]. Structural Health Monitoring, 20, 692-743(2021).

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

[3] Wang S, Wu X, Zhang Y H et al. Surface crack segmentation based on multi-scale wavelet transform and structured forest[J]. Acta Optica Sinica, 38, 0815024(2018).

[4] Liu F, Wu Z W, Yang A Z et al. Multi-scale feature fusion based adaptive object detection for UAV[J]. Acta Optica Sinica, 40, 1015002(2020).

[5] Chi H. Online detection method of woven bag defects based on machine vision[J]. Laser & Optoelectronics Progress, 57, 201507(2020).

[6] Wu J, Li X, Liu S Y et al. Global three-dimensional reconstruction method for visual detection of aircraft skin damage based on rear positioning[J]. Acta Optica Sinica, 41, 1115002(2021).

[7] Chen M Y, Tang Y C, Zou X J et al. High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm[J]. Optics and Lasers in Engineering, 122, 170-183(2019).

[8] Chen M Y, Tang Y C, Zou X J et al. Multi-shaped targets recognition and point clouds acquisition algorithm in complex environment[J]. Laser & Optoelectronics Progress, 55, 111505(2018).

[9] Tang Y C, Li L J, Feng W X et al. Binocular vision measurement and its application in full-field convex deformation of concrete-filled steel tubular columns[J]. Measurement, 130, 372-383(2018).

[10] Liang X M, Liu W T, Niu F S et al. Research on measurement of volume and surface area of flotation bubbles based on machine vision[J]. Acta Optica Sinica, 38, 1215009(2018).

[11] Wang W J, Zhang A, Wang K C P et al. Pavement crack width measurement based on Laplace’s equation for continuity and unambiguity[J]. Computer-Aided Civil and Infrastructure Engineering, 33, 110-123(2018).

[12] Kim H, Ahn E, Cho S et al. Comparative analysis of image binarization methods for crack identification in concrete structures[J]. Cement and Concrete Research, 99, 53-61(2017).

[13] Zou Q, Zhang Z, Li Q et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 28, 1498-1512(2018).

[14] Zhang A, Wang K C P, Fei Y et al. Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network[J]. Computer-Aided Civil and Infrastructure Engineering, 34, 213-229(2019).

[15] Wang M Z, Cheng J C P. A unified convolutional neural network integrated with conditional random field for pipe defect segmentation[J]. Computer-Aided Civil and Infrastructure Engineering, 35, 162-177(2020).

[16] Li Q, Zeng S G, Zheng S et al. Machine vision based detection method for surface crack of ceramic tile[J]. Laser & Optoelectronics Progress, 57, 081004(2020).

[17] Qiu S, Wang W J, Wang S F et al. Methodology for accurate AASHTO PP67-10-based cracking quantification using 1-mm 3D pavement images[J]. Journal of Computing in Civil Engineering, 31, 04016056(2017).

[18] Zhou Y, Liu T. Computer vision-based crack detection and measurement on concrete structure[J]. Journal of Tongji University (Natural Science), 47, 1277-1285(2019).

[19] Kim H, Lee J, Ahn E et al. Concrete crack identification using a UAV incorporating hybrid image processing[J]. Sensors, 17, 2052(2017).

[20] Zhu S Y, Du J C, Li Y S et al. Method for bridge crack detection based on the U-Net convolutional networks[J]. Journal of Xidian University, 46, 35-42(2019).

[21] Luo Q J, Ge B Z, Tian Q G. A fast adaptive crack detection algorithm based on a double-edge extraction operator of FSM[J]. Construction and Building Materials, 204, 244-254(2019).

[22] Lei Z Y. Visual detection of dam surface cracks on complex background based on deep learning[D](2020).

[23] Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns[J]. Communications of the ACM, 27, 236-239(1984).