Optics and Precision Engineering, Volume. 26, Issue 12, 3040(2018)

Pedestrian intruding railway clearance classification algorithm based on improved deep convolutional network

GUO Bao-qing1...2,* and WANG Ning12 |Show fewer author(s)
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  • 1[in Chinese]
  • 2[in Chinese]
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    References(19)

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    [3] [3] SHI H M, CHAI H, WANG Y, et al.. Study on railway embedded detection algorithm for railway intrusion based on object recognition and tracking[J]. Journal Of The China Rallway Society, 2015, 37(7): 59-65.(in Chinese)

    [4] [4] NING B , YU Z J, ZHU L Q, et al..Remote observation system of railway and its application[J]. Journal of The China Railway Society, 2014.36(12): 62-69.(in Chinese)

    [5] [5] Guo B Q, Yang L X, Shi H M, et al.. High-speed railway clearance intrusion detection algorithm with fast background subtraction[J]. Chinese Journal of Scientific Instrument, 2016, 37(6): 1371-1378. (in Chinese)

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    [11] [11] TIAN Y, GELEMETER J , WANG X, et al.. Lane marking detection via deep convolutional neural network [J].Neurocomputing. 2018, 46: 46-55.

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    [15] [15] WANG Y, YU Z Y, ZHU L Q, et al.. Fast feature extraction algorithm for high-speed railway clearance intruding objects based on CNN[J]. Chinese Journal of Scientific Instrument, 2017, 5(38): 1267-1275. (in Chinese)

    [16] [16] RUI T, ZOU J H, ZHOU Y. Pedestrian detection based on multi-convolutional features by feature maps pruning[J]. Multimedia Tools And Applications, 2017, 76(23): 25079-25089.

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    GUO Bao-qing, WANG Ning. Pedestrian intruding railway clearance classification algorithm based on improved deep convolutional network[J]. Optics and Precision Engineering, 2018, 26(12): 3040

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    Paper Information

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    Received: Apr. 27, 2018

    Accepted: --

    Published Online: Jan. 27, 2019

    The Author Email: Bao-qing GUO (bqguo@bjtu.edu.cn)

    DOI:10.3788/ope.20182612.3040

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