Abstract
Forward train detection is of great significance to improve train safety. In this paper, a vision-based method is proposed for forward train detection. The proposed method based on CenterNet, a novel object detection network, to realize accurate and fast forward train detection. The forward train detection network is divided into two stages: downsampling stage and center points generation stage. In downsampling stage, a full convolution network is performed to downsample the image, meanwhile to extract the feature of the image. In the center points generation stage, three branch networks are used to predict the bounding box of the forward train, including heatmaps generation branch, center offset regression branch and forward train bounding box size regression branch. Experiments results show that the proposed method can detect the forward train well and achieve 30.7% Average Precision (AP) with a 47.6 Frames Per Second (FPS).
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García, J.J., Urena, J., Mazo, M., et al.: Sensory system for obstacle detection on high-speed lines. Transp. Res. Part C Emerg. Technol. 18(4), 536–553 (2010)
Li, S., Cai, B., Liu, J., et al.: Collision risk analysis based train collision early warning strategy. Accid. Anal. Prev. 112, 94–104 (2018)
Lüy, M., Çam, E., Ulamış, F., et al.: Initial results of testing a multilayer laser scanner in a collision avoidance system for light rail vehicles. Appl. Sci. 8(4), 475 (2018)
Karaduman, M.: Image processing based obstacle detection with laser measurement in railways. In: 2017 10th International Conference on Electrical and Electronics Engineering (ELECO), pp. 899–903. IEEE (2017)
Garcia, J.J., Ureña, J., Hernandez, A., et al.: Efficient multisensory barrier for obstacle detection on railways. IEEE Trans. Intell. Transp. Syst. 11(3), 702–713 (2010)
Yao, T., Dai, S., Wang, P., et al.: Image based obstacle detection for automatic train supervision. In: 2012 5th International Congress on Image and Signal Processing, pp. 1267–1270. IEEE (2012)
Zhou, X., et al.: Objects as points. arXiv preprint arXiv:1904.07850v2
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Yu, F., Wang, D., Shelhamer, E., et al.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2018)
Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) European Conference on Computer Vision, pp. 740–755. Springer, Cham (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Acknowledgments
This work is partially supported by the Beijing Municipal Science and Technology Project under Grant # Z181100008918003. The MTR Corporation Ltd. in Hong Kong has provided the testing field in co-researching the proposed forward train detection method and technology. The authors would also like to thank the insightful and constructive comments from anonymous reviewers.
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Wang, Z. et al. (2020). A Forward Train Detection Method Based on Convolutional Neural Network. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_21
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DOI: https://doi.org/10.1007/978-3-030-39512-4_21
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