[1] 王猛, 刘佳星, 侯雪情, 等. 高铁沿线突发事件形成机制[J]. 中国安全科学学报, 2020, 30(增刊1): 179-187. Wang M, Liu J X, Hou X Q, et al. Formation mechanism of emergencies along high-speed rail [J]. China Safety Science Journal, 2020, 30(Suppl.1): 179-187. (in Chinese) [2] Ren K Y, Hou H C, Li S Y, et al. LaneDraw: cascaded lane and its bifurcation detection with nested fusion [J]. Science China Technological Sciences, 2021, 64(6): 1238-1249. [3] Ghanem S, Kanungo P, Panda G, et al. An improved and low-complexity neural network model for curved lane detection of autonomous driving system [J]. Soft Computing, 2023, 27(1): 493-504. [4] Lu P P, Xu S B, Peng H E. Graph-embedded lane detection [J]. IEEE Transactions on Image Processing, 2021, 30: 2977-2988. [5] Maire F, Bigdeli A. Obstacle-free range determination for rail track maintenance vehicles [C]//International Conference on Control Automation Robotics & Vision, 2011: 2172-2178. [6] Espino J C, Stanciulescu B, Forin P. Rail and turnout detection using gradient information and template matching [C]//IEEE International Conference on Intelligent Rail Transportation Proceedings, 2013: 233-238. [7] Espino J C, Stanciulescu B. Rail extraction technique using gradient information and a priori shape model [C]//IEEE Conference on Intelligent Transportation Systems, 2012: 1132-1136. [8] Nassu B T, Ukai M. Rail extraction for driver support in railways [C]//IEEE Intelligent Vehicles Symposium (IV), 2011: 83-88. [9] Qi Z Q, Tian Y J, Shi Y. Efficient railway tracks detection and turnouts recognition method using HOG features [J]. Neural Computing and Applications, 2013, 23(1): 245-254. [10] Zwemer M H, Van De Wouw D W J M, Jaspers E G T, et al. A vision-based approach for tramway rail extraction [C]//Video Surveillance and Transportation Imaging Applications, 2015, 9407: 227-239. [11] Wang Z Y, Wu X K, Yu G Z, et al. Efficient rail area detection using convolutional neural network [J]. IEEE Access, 2018, 6: 77656-77664. [12] Wang Y,Wang L D, Hu Y H, et al. RailNet: a segmentation network for railroad detection [J]. IEEE Access, 2019, 7: 143772-143779. [13] Li X X, Zhu L Q, Yu Z J, et al. Vanishing point detection and rail segmentation based on deep multi-task learning [J]. IEEE Access, 2020, 8: 163015-163025. [14] Romera E, Alvarez J M, Bergasa L M, et al. ERFNet: efficient residual factorized Conv- Net for real-time semantic segmentation [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(1): 263-272. [15] Tastimur C, Karakose M, Akin E. A vision based condition monitoring approach for rail switch and level crossing using hierarchical SVM in railways [J]. International Journal of Applied Mathematics Electronics and Computers, 2016: 319-325. [16] Zhang S J, Tang T, Liu J T. A hazard analysis approach for the SOTIF in intelligent railway driving assistance systems using STPA and complex network [J]. Applied Sciences, 2021, 11(16): 7714. [17] Zhu X Z, Su W J, Lu L W, et al. Deformable DETR: deformable transformers for end-to-end object detection [DB/OL]. 2020[2023-06-29]. https://arxiv.org/abs/2010.04159. [18] Wang Q L, Wu B G, Zhu P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks [C]//IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2020: 11534-11542. [19] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [DB/OL]. 2017[2023-06-29]. https://arxiv.org/abs/1706.03762. [20] Guo Z C, Hall R W. Parallel thinning with two-subiteration algorithms [J]. Communications of the ACM, 1989, 32(3): 359-373. [21] Zhou T Y, Zhao Y, Wu J. ResNeXt and Res2Net structures for speaker verification [C]//IEEE Spoken Language Technology Workshop (SLT), 2021: 301-307. [22] Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module [C]//European Conference on Computer Vision (ECCV). Cham: Springer, 2018: 3-19. [23] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440. [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, 2017, 39(12): 2481-2495. [25] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN [C]//IEEE International Conference on Computer Vision, 2017: 2961-2969. [26] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation [C]//European Conference on Computer Vision (ECCV). Cham: Springer, 2018: 801-818. [27] Yang S Y, Yu G Z, Wang Z Y, et al. A topology guided method for rail-track detection [J]. IEEE Transactions on Vehicular Technology, 2021, 71(2): 1426-1438. [28] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [DB/OL]. 2014[2023-06-29]. https://arxiv.org/abs/1409.1556. [29] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826. [30] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778. |