[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 |