Volume 41 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(2): 18-27
DAI Yuan, LIU Weiming, WANG Heng, XIE Wei, LONG Kejun. A Method for Timely Detecting Foreign Objects between Metro Platform Screen Doors and Train Doors Based on an Improved YOLOv5s Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 18-27. doi: 10.3963/j.jssn.1674-4861.2023.02.002
Citation: DAI Yuan, LIU Weiming, WANG Heng, XIE Wei, LONG Kejun. A Method for Timely Detecting Foreign Objects between Metro Platform Screen Doors and Train Doors Based on an Improved YOLOv5s Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 18-27. doi: 10.3963/j.jssn.1674-4861.2023.02.002
shu

A Method for Timely Detecting Foreign Objects between Metro Platform Screen Doors and Train Doors Based on an Improved YOLOv5s Model

doi: 10.3963/j.jssn.1674-4861.2023.02.002
  • 1.

    School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China

  • 2.

    Shenzhen Metro Group Co., Ltd., Shenzhen 518026, Guangdong, China

  • 3.

    School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China

  • Received Date: 2022-08-29
    Available Online: 2023-06-19
    Abstract
  • Accurately and efficiently detecting foreign objects between platform screen doors (PSDs) and train doors at metro stations is of great significance for safety purpose. In response to the inefficiency and inaccuracy of current detection methods, a method based on the you-only-look-once (YOLOv5s) model is proposed. As the original YOLOv5s model relies on internal features of candidate regions but not global contextual information, a global context module is introduced to address the limitation. This module integrates non-local modules and squeeze-excitation modules. The non-local modules use self-attention mechanism to model relationships between pixels and capture long-term dependencies. The squeeze-excitation modules is developed to reduce the computational cost of the model. The global context module enables the model to capture global contextual information and combines it with local information for improved detection of foreign objects without significantly increasing computational complexity. Additionally, the inefficient Focus module of the original YOLOv5s is replaced with a Stem module that is fully developed from standard convolutional units, contributing to a reduced computation cost and enhanced detection speed. Experiments are conducted based on a dataset of 5 854 foreign object images collected from metro stations, with the model being tested using desktop-level NVIDIA TITAN Xp graphics cards. The results indicate that ①the improved YOLO model performs remarkably better than other baseline models, exhibiting an impressive detection speed of 385 frames per second, a 100% improvement over the original YOLOv5s model and a substantial 466% improvement over the fastest speed of YOLOv3-SPP model. ② The improved YOLO model achieves an average detection accuracy of 88.5%, a 0.5% improvement over the original YOLOv5s and a 0.6% improvement over the highest average detection accuracy of YOLOv3-SPP. ③ The improved YOLO model takes up only 14.4 MB of computer storage space, which is 0.7% less than the original YOLOv5s, and 85% less than the single shot multibox detector (SSD) that takes the least storage space.

     

    • FullText(HTML)
  • loading
    • References(27)
  • [1]
    刘伟铭, 陈纲梅, 李海玉, 等. 地铁风险空间分析及异物检测系统技术要求[J]. 铁道标准设计, 2019, 63(10): 168-176. https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS201910032.htm

    LIU W M, CHEN G M, LI H Y, et al. Risk space analysis and technical requirements for foreign object detection system[J]. Railway Standard Design, 2019, 63(10): 168-176. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS201910032.htm
    [2]
    上海地铁. "1月22日"情况说明[EB/OL]. (2022-01-25)[2022-08-27]. https://wei-bo.com/shmetro.

    Shanghai metro. Factsheet for January 22[EB/OL]. (2022-01-25)[2022-08-27]. https://we-ibo.com/shmetro. (in Chinese)
    [3]
    李海玉, 刘伟铭, 李军, 等. 曲线地铁站台屏蔽门与列车间异物自动检测装置及方法: 201410314715. 4[P]. 2017-07-07.

    LI H Y, LIU W M, LI J, et al. Device and method for automatic detection of foreign objects between platform screen doors of curved metro platforms and trains: 201410314715. 4[P]. 2017-07-07(in Chinese)
    [4]
    谭飞刚, 刘建. 1种基于计算机视觉的地铁站台异物检测算法[J]. 铁路计算机应用, 2017, 26(1): 67-69. https://www.cnki.com.cn/Article/CJFDTOTAL-TLJS201701020.htm

    TAN F G, LIU J. Foreign object detection algorithm for subway platform based on computer vision[J]. Railway Computer Application, 2017, 26(1): 67-69. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TLJS201701020.htm
    [5]
    刘伟铭, 杜逍睿, 李静宁, 等. SOM与HL融合的地铁异物分类算法[J]. 铁道标准设计, 2020, 64(7): 161-165. https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS202007029.htm

    LIU W M, DU X R, LI J N, et al. Subway foreign object classification based on som and hl fusion[J]. Railway Standard Design, 2020, 64(7): 161-165. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS202007029.htm
    [6]
    杨鹏强, 张艳伟, 胡钊政. 基于改进RepVGG网络的车道线检测算法[J]. 交通信息与安全, 2022, 40(2): 73-81. doi: 10.3963/j.jssn.1674-4861.2022.02.009

    YANG P Q, ZHANG Y W, HU Z Z. A lane detection algorithm based on improved repvgg network[J]. Journal of Transport Information and Safety, 2022, 40(2): 73-81. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.02.009
    [7]
    王鹏, 神和龙, 尹勇, 等. 基于深度学习的船舶驾驶员疲劳检测算法[J]. 交通信息与安全, 2022, 40(1): 63-71. doi: 10.3963/j.jssn.1674-4861.2022.01.008

    WANG P, SHEN H L, YIN Y, et al. A detection algorithm for the fatigue of ship officers based on deep learning technique[J]. Jour-nal of Transport Information and Safety, 2022, 40(1): 63-71. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.01.008
    [8]
    崔晓宁, 王起才, 李盛, 等. 基于YOLO-v5的双块式轨枕裂缝智能识别[J]. 铁道学报, 2022, 44(4): 104-111. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202204013.htm

    CUI X N, WANG Q C, LI S, et al. Intelligent recognition of cracks in double block sleeper based onYOLO-v5[J]. Journal of the China Railway Society, 2022, 44(4): 104-111. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202204013.htm
    [9]
    DAI Y, LIU W M, LI H Y, et al. Efficient foreign object detection between psds and metro doors via deep neural networks[J]. IEEE Access, 2020(8): 46723-46734.
    [10]
    刘伟铭, 温俊锐, 郑仲星, 等. 适用于地铁异物前景检测的神经网络: DifferentNet[J]. 华南理工大学学报(自然科学版), 2021, 49(10): 11-21, 40. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202110002.htm

    LIU W M, WEN J R, ZHENG Z X, et al. Differentnet: neural network for foreign objects foreground detection in metro[J]. Journal of South China University of Technology(Natural Science Edition), 2021, 49(10): 11-21, 40. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202110002.htm
    [11]
    LIU R K, LIU W M, LI H Y, et al. Metro anomaly detection based on light strip inductive key frame extraction and magan network[J]. IEEE Transactions on Instrumentation and Measurement, 2022(71): 1-14.
    [12]
    LIU W, ANGUELOV D, ERHAN D, et al. Ssd: single shot multibox detector[C]. The European Conference on Computer Vision, Amsterdam, The Netherlands: Springer, 2016.
    [13]
    REDMON J, FARHADI A. Yolov3: an incre-mental improvement[EB/OL]. (2018-04-08)[2022-08-27]. https://arxiv.org/pdf/1804.02767.pdf
    [14]
    BOCHKOVSKI A, WANG C Y, LIAO H Y. Yolov4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2022-08-27]. https://arxiv.org/pdf/2004.10934.pdf.
    [15]
    Ultralytics. Yolov5[CP/OL]. (2022-02-0-9)[2022-08-27]. https://github.com/ultralytics/yolov5.
    [16]
    LI J N, WEI Y C, LIANG X D, et al. Att-entive contexts for object detection[J]. IEEE Transactions on Multimedia, 2017, 19(5): 944-954.
    [17]
    SEAN B C, LAWRENCE Z, KAVITA B, et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks[C]. The IEEE/CVF conference on Computer Vision and Pattern Recognition, Las Vegas, USA: IEEE, 2016.
    [18]
    CHEN Q, ZHENG S, JI D, et al. Contextualizing object detection and classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 31(1), 13-27.
    [19]
    HAN C, LIN J, LIN Y J, et al. Enable deep learning on mobile devices: methods, systems, and applications[J]. ACM Transactions on Design Automation of Electronic Systems, 2022, 27(3): 1-50.
    [20]
    CAO Y, XU J R, LIN S, et al. Gcnet: non-local networks meet squeeze-excitation networks and beyond[C]. IEEE/CVF International Conference on Computer Vision Workshops, Seoul, Korea(South): IEEE, 2019.
    [21]
    WANG J, BOHN T A, LING C X. Pelee: a real-time object detection system on mobile devices[C]. Advances in Neural Information Processing Systems, Montréal, Canada: NeurIPS Foundation, 2018.
    [22]
    WANG C Y, LIAO H Y, WU Y H, et al. Cspnet: a new backbone that can enhance learning capability of cnn[C]. IEEE/CVF conference on Computer Vision and Pattern Recognition workshops, Seattle, USA: IEEE, 2020.
    [23]
    ZHENG Z H, WANG P, LIU W, et al. Distance-iou loss: faster and better learning for bounding box regression[C]. AAAI Conference on Artificial Intelligence, New York, USA: AAAI, 2020.
    [24]
    WANG X L, GIRSHICK R B, GUPTA A, et al. Non-local neural networks[C]. IEEE/CVF conference on Computer Vision and Pattern Recognition, Salt Lake City, USA: IEEE, 2018.
    [25]
    HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]. IEEE/CVF conference on Computer Vision and Pattern Recognition, Salt Lake City, USA: IEEE, 2018.
    [26]
    GE Z, LIU S T, WANG F, et al. Yolox: exceeding yolo series in 2021[EB/OL]. (2021-08-06)[2022-08-27]. https://arxiv.org/pdf/2107.08430.pdf.
    [27]
    SIMONYAN K, ANDREW Z. Very deep convolutional networks for large-scale image recognition[C]. International Conference on Learning Representations, San Diego, USA: ICLR, 2015.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (934) PDF downloads(91) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return