Abstract
With the rapid development of high-speed railway, its operation management and traffic safety are becoming more and more important. Foreign matter intrusion around high-speed railway needs to be timely prevented and identified. The current intrusion target recognition system still has the problems of high miss rate and false detection rate for the recognition of small intrusion targets beyond 100m. In this paper, aiming at this practical problem, combined with the deep learning target detection algorithm and the requirements of high-speed railway perimeter prevention and control, a system with better intrusion target recognition effect is designed to make up for the shortcomings of current high-speed railway perimeter video monitoring. Based on the actual video monitoring picture of high-speed railway, this experiment establishes a data set of far and small intrusion target recognition of high-speed railway perimeter, and establishes a complete evaluation standard system. Through the selection of algorithm model, it improves the ability of far and small intrusion target recognition of high-speed railway perimeter, and combines the algorithm to establish a far and small intrusion target recognition system of high-speed railway perimeter.
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References
Salmane, H., Khoudour, L., Ruichek, Y., et al.: A video-analysis-based railway-road safety system for detecting hazard situations at level crossings. IEEE Trans. Intell. Transp. Syst. 16(2), 596–609 (2015)
Li, H., Achim, A., Bull, D.R.: GMM-based efficient foreground detection with adaptive region update, 2009: 3181–3184
Nguyen, T.B., Van Nguyen, T., Chung, S.T.: A real-time pedestr ian detection based on agmm and hog for embedded surveillance. J. Korea Multimed. Soc., 18(11), 1289–1301 (2015)
Ciresan, D., Meier, U., Masci, J., et al.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
Liu, Y.: Study on an improved monitoring scheme for foreign matter intrusion into railway. Railw. Commun. Signal Eng. Technol., 10(2), 30–32 (2013)
Jianming, Q., Zhijing, L., Wenhua, H.: Directed weighted adaBoost target detection combined with scene motion mode. J. Xidian Univ. 42(3), 67–72 (2015)
Huang, L., Mao, X., Hang, D. etc.: Research on star catalog unstructured rock target identification method based on deep learning network. Space Control. Technol. Appl., 47(6), 27–33 (2021)
Aiping, Y., Shangyang, S., Simeng, C.: Lightweight adaptive feature selection target detection network. J. Northeast. Univ. (Nat. Sci. Ed.) 42(9), 1238–1245 (2021)
Jiang, J., Fu, X, Qin, R. et al.: High-speed lightweight ship detection algorithm based on yolo-v4 for three-channels RGB SAR image. Remote Sensing, 13(10), (2021)
Tao, Z., Sun, S., Luo, C.: Research on peanut pest image recognition based on Faster RCNN. Jiangsu Agric. Sci., 47(12), 247–250 (2019)
Zhang, Y., Du, H., Sun, Y. etc.: Remote sensing image target detection based on improved SSD algorithm. Comput. Eng., 47(9), 252–258, 265 (2021)
Gao, B., Zheng, K., Zhang, F., Su, R., Zhang, J., Wu, Y.: Research on multi-target tracking method based on multi-sensor fusion. Smart Resilient Transp. 4(2), 46–65 (2022)
Jia, L., et al.: On autonomous transportation systems. Smart Resilient Transp. 4(2), 66–77 (2022)
Zhao, R., Ma, X., Zhang, H., Dong, H., Qin, Y., Jia, L.: Enhanced densely dehazing network for single image haze removal under railway scenes. Smart Resilient Transp. 3(3), 218–234 (2021)
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This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFF0304104.
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Lu, M., Xie, Z. (2023). Recognition of Remote and Small Intrusion Targets Around High-Speed Railway Based on Deep Learning Method. In: Sun, F., Yang, Q., Dahlquist, E., Xiong, R. (eds) The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles (ICEIV 2022). ICEIV 2022. Lecture Notes in Electrical Engineering, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-99-1027-4_98
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DOI: https://doi.org/10.1007/978-981-99-1027-4_98
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