基于深度学习算法的铁路列车运行安全检测*

doi:10.16265/j.cnki.issn1003-3033.2018.S2.008

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (S2): 41-45.doi: 10.16265/j.cnki.issn1003-3033.2018.S2.008

基于深度学习算法的铁路列车运行安全检测^*

汪洋¹ 高级工程师, 王俊刚² 提高待遇高工

1 国家铁路局规划与标准研究院,北京 100055;
2 铁路总公司中国铁路北京局集团有限公司,北京 100860

收稿日期:2018-09-19 修回日期:2018-11-13 出版日期:2018-12-30
作者简介:汪洋 (1975—),男,浙江金华人,硕士,高级工程师,主要从事交通运输方面的工作。E-mail:13661380301@139.com。
基金资助:
中国铁路总公司科技研究开发计划项目(2017X014-A)。

Study on safety inspection of railway train operation based on deep learning algorithm

WANG Yang¹, WANG Jungang²

1 Planning and Standard Research Institute, National Railway Administration, Beijing 100055, China;
2 China Railway Beijing Group Co., Ltd., China Railway Corporation, Beijing 100860, China

Received:2018-09-19 Revised:2018-11-13 Published:2018-12-30

摘要/Abstract

摘要： 随着铁路列车运行安全要求的不断提高,迫切需要及时检测并处理列车运行中的安全隐患。传统的铁路列车运行自动化检测手段主要基于机器视觉算法,存在对列车运行的复杂自然场景适应能力差、误报率高等问题。为解决传统方法检测精度较低等问题,模拟多层神经网络传递结构,输入和识别隐患扫描素材,提出基于深度学习算法的列车运行安全检测方法;然后以铁路货车异物检测为例进行仿真试验,验证相关算法。结果表明:新检测方法在保证检出率的前提下降低了误报率。

关键词: 列车, 安全检测, 深度学习, 混淆矩阵, 迭代

Abstract: With the continuous improvement of railway train operation safety requirements, it is urgent to detect and deal with hidden dangers in train operation in time. The traditional automatic detection method of railway train operation was mainly based on machine vision algorithm, which had the problems of poor adaptability to complex natural scenes and high false alarm rate. In order to solve the problem of low detection accuracy of traditional methods, multi-layer neural network transmission structure was simulated, where hidden trouble scanning materials were input and identified, and a train operation safety detection method based on deep learning algorithm was proposed. Then, a simulation experiment is carried out to verify the relevant algorithm by taking the detection of foreign matter as an example. The results show that the new detection method can reduce the false alarm rate under the premise of ensuring the detection rate.

Key words: train, safety inspection, deep learning, confusion matrix, iteration

中图分类号:

X924.2

汪洋, 王俊刚. 基于深度学习算法的铁路列车运行安全检测^*[J]. 中国安全科学学报, 2018, 28(S2): 41-45.

WANG Yang, WANG Jungang. Study on safety inspection of railway train operation based on deep learning algorithm[J]. China Safety Science Journal, 2018, 28(S2): 41-45.

参考文献

[1] MACQUEEN J. Some methods for classification and analysis of multivariate observations[C]. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics & Probability, 1965:281-297.
[2] WELLS K, BRADLEY D A. A review of X-ray explosives detection techniques for checked baggage[J]. Applied Radiation & Isotopes, 2012, 70(8): 1 729-1 746.
[3] GOODFELLOW I, BENGIO Y, COURVILLE A. Deep learning[M].Cambridge: The MIT Press, 2016:68-95.
[4] 朱海港.基于深度学习的高清航拍图像目标检测[D].北京:中国科学院大学,2015.
ZHU Haigang. Research on high resolution aerial objection based on deep learning[D]. Beijing: University of Chinese Academy of Sciences, 2015.
[5] 黄峰,徐悦竹.图像识别技术在铁路设备故障检测中的应用[J].信息技术,2013(7):147-149.
HUANG Feng, XU Yuezhu. Image recognition technology in the railway equipment fault detection[J].Information Technology, 2013(7): 147-149.
[6] 佟瑞鹏,陈策,崔鹏程,等.基于深度学习的施工安全泛场景数据获取方法[J].中国安全科学学报,2017,27(5):1-6.
TONG Ruipeng, CHEN Ce, CUI Pengcheng, et al. Deep learning method for processing pan-scene data on construction safety[J]. China Safety Science Journal, 2017, 27(5): 1-6.
[7] KRIZHEVSKY A. Convolutional deep belief networks on CIFAR-10[J]. Communications of the Acm, 2010, 54(10):95-103.
[8] BURGES C J C. From ranknet to lambdarank to lambdamart: an overview[J]. Learning, 2010, 11: 23-41.
[9] YAN J, XU N Y, CAI X F, et al. An FPGA-based accelerator for LambdaRank in Web search engines[J]. Acm Transactions on Reconfigurable Technology & Systems, 2011, 4(3): 2 658-2 674.
[10] 张瑜,罗可.基于OC-SVM的大型数据集分类方法[J].计算机工程与应用,2011,47(4):131-133.
ZHANG Yu, LUO Ke. OC-SVM-based classification for large-scale data sets[J]. Computer Engineering & Applications, 2011,47(4): 131-133.
[11] GRAY C, BEATTIE M, BELAY H, et al. Personalized online search for fashion products[C]. Systems and Information Engineering Design Symposium (SIEDS), 2015. IEEE, 2015:91-96.

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基于深度学习算法的铁路列车运行安全检测^*

Study on safety inspection of railway train operation based on deep learning algorithm

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