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基于深度学习的高效火车号识别

王志明 刘志辉 黄洋科 邢宇翔

王志明, 刘志辉, 黄洋科, 邢宇翔. 基于深度学习的高效火车号识别[J]. 工程科学学报, 2020, 42(11): 1525-1533. doi: 10.13374/j.issn2095-9389.2019.12.05.001
引用本文: 王志明, 刘志辉, 黄洋科, 邢宇翔. 基于深度学习的高效火车号识别[J]. 工程科学学报, 2020, 42(11): 1525-1533. doi: 10.13374/j.issn2095-9389.2019.12.05.001
WANG Zhi-ming, LIU Zhi-hui, HUANG Yang-ke, XING Yu-xiang. Efficient wagon number recognition based on deep learning[J]. Chinese Journal of Engineering, 2020, 42(11): 1525-1533. doi: 10.13374/j.issn2095-9389.2019.12.05.001
Citation: WANG Zhi-ming, LIU Zhi-hui, HUANG Yang-ke, XING Yu-xiang. Efficient wagon number recognition based on deep learning[J]. Chinese Journal of Engineering, 2020, 42(11): 1525-1533. doi: 10.13374/j.issn2095-9389.2019.12.05.001

基于深度学习的高效火车号识别

doi: 10.13374/j.issn2095-9389.2019.12.05.001
详细信息
    通讯作者:

    E-mail:wangzhiming@ustb.edu.cn

  • 中图分类号: TP391

Efficient wagon number recognition based on deep learning

More Information
  • 摘要: 基于高性能的YOLOv3目标检测算法,提出一种分阶段高效火车号识别算法。整个识别过程分为两个阶段:第一阶段在低分辨率全局图像中检测出火车号区域位置;第二阶段在局部高分辨率图像中检测出组成火车号的字符,根据字符的空间位置关系搜索得到12位火车号,并利用每个字符的识别置信度及火车号编码规则进行校验得到最终火车号。另外,本文提出一种结合批一化因子和滤波器相关度的剪枝算法,通过对两个阶段检测模型的剪枝,在保证识别准确率不降(实验中略有提升)的条件下降低了存储空间占用率和计算复杂度。在现场采集的1072幅火车号图像上的实验结果表明,本文提出的火车号识别算法达到了96.92%的整车号识别正确率,平均识别时间仅为191 ms。
  • 图  1  火车号图像示例

    Figure  1.  Example of a wagon number image

    图  2  火车号识别流程

    Figure  2.  Pipeline of the wagon number recognition

    图  3  火车号分布的热力图

    Figure  3.  Heatmap of the wagon number distribution

    图  4  火车号区域检测图像

    Figure  4.  Images for the wagon number region detection

    图  5  火车号区域检测结果。(a)区域检测结果;(b)提取到的局部区域图

    Figure  5.  Wagon number region detection results: (a) region detection results; (b) extracted region image

    图  6  火车号字符检测结果示例

    Figure  6.  Examples of wagon number character detection results

    图  7  每个字符对应的前8个最大概率类别、概率值及校验纠错位

    Figure  7.  Top 8 class and corresponding probabilities of every character and correction by verification

    表  1  火车号区域检测和字符检测的实验结果

    Table  1.   Results of wagon number region detection and character detection

    PhaseDetectionmodelPruningmAP/%Model size/MBRuntime memory/MBMean time/ms
    Region detectionYOLOv3N95.31241162544.06
    Y95.3793115531.41
    Faster-RCNN95.383231121103.16
    SSD95.2019283362.01
    Character detectionYOLOv3N90.39241130729.22
    Y90.698488118.43
    Faster-RCNN90.683231161110.09
    SSD90.4020185162.49
    下载: 导出CSV

    表  2  校验和模型剪枝对识别结果的影响

    Table  2.   Influence of model pruning and verification on the recognition results

    Verification & correctionPruningAccuracy rate/%Error rate/%Rejection rate/%Mean time/ms
    NN93.564.761.68221
    YN96.361.961.68224
    YY96.922.150.93191
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-05
  • 刊出日期:  2020-11-25

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