[1] Koyama Y J T. Present status and technology of shield tunneling method in Japan [J]. Tunnelling and Underground Space Technology, 2003, 18(2/3): 145-159. doi:  10.1016/S0886-7798(03)00040-3
[2] Wada M. Automatic segment erection system for shield tunnels [J]. Advanced Robotics, 1990, 5(4): 429-443. doi:  10.1163/156855391X00304
[3] Yasuo Tanaka. Automatic segment assembly robot for shield tunneling machine [J]. Computer-Aided Civil and Infrastructure Engineering, 2010, 10(5): 325-337. doi:  https://doi.org/10.1111/j.1467-8667.1995.tb00295.x
[4] Wu Z, Zhang L, Wang S, et al. Automatic segment assembly method of shield tunneling machine based on multiple optoelectronic sensors[C]//International Conference on Optical Instruments and Technology 2019: Optical Sensor and Applications, 2019, 11436: 14360U.
[5] Chow C K, Kaneko T. Boundary detection of radiographic images by a threshold method[M]//Frontiers of Pattern Recognition. Cambridge, Massachusetts: Academic Press, 1972: 61-82.
[6] Zhang D D, Zhao S. An improved edge detection algorithm based on Canny operator [J]. Applied Mechanics and Materials, 2013, 347-350(4): 3541-3545. doi:  https://doi.org/10.4028/www.scientific.net/AMM.347-350.3541
[7] Carson C, Thomas M, Belongie S, et al. Blobworld: A system for region-based image indexing and retrieval[M]//Visual Information and Information Systems: Proceedings of the Third International Conference on Visual Information and Information Systems. Switzerland: Springer, 1999.
[8] Yan Y, Liu G, Wang S, et al. Graph-based clustering and ranking for diversified image search [J]. Multimedia Systems, 2014, 23(1): 41-52.
[9] Smith L N. Cyclical learning rates for training neural networks[C]//2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 2017: 464-472.
[10] Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.
[11] He K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision (ICCV), 2017.
[12] X. Li, H. Li, Y. Lin, et al. Learning-based denoising for polarimetric images [J]. Opt Express, 2020, 28: 16309-16321. doi:  10.1364/OE.391017
[13] Zhang Y, Tian Y, Kong Y, et al. Residual dense network for image restoration [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(7): 2480-2495. doi:  10.1109/TPAMI.2020.2968521
[14] Hu H, Zhang Y, Li X, et al. Polarimetric underwater image recovery via deep learning [J]. Optics and Lasers in Engineering, 2020, 133: 106152. doi:  https://doi.org/10.1016/j.optlaseng.2020.106152