Volume 41 Issue 2
Apr.  2023
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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.

     

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