Automated detection of bicycle helmets using deep learning

Bicycle helmets are a main measure for injury prevention in case of a crash and are a central variable in transport safety studies. Despite this, helmet use data is only collected sporadically, as the observation of helmet use in traffic by human observers is costly and time-consuming. An automated method for the accurate registration of bicycle helmet use would enable the broad and precise registration of cyclists’ helmet use. In this paper, the authors develop and test a computer vision-based detection method that can be applied to traffic video data. They record bicycle traffic at two observation sites in Copenhagen, Denmark, and annotate a dataset of 4000 cyclists, registering their helmet use. The authors then train a state-of-the-art object detection algorithm on the detection of cyclists and helmet use. The developed model has good accuracy in registering active cyclists. For helmet use registration on the test data set, there was an underestimation of 0.52% (algorithm registered helmet use: 50.23%; actual helmet use: 50.75%). Cross-testing the algorithm, i.e., training on one observation site and applying it to another, results in a larger underestimation of bicycle helmet use between 5.28% and 6.31%. Finally, the authors apply the algorithm to a week of video data from two Copenhagen sites, identifying commuting-related peaks of cyclists and registering helmet use differences between the observation sites. This study shows that computer vision algorithms are a feasible method for the automated detection of bicycle helmet use. Further research needs to be conducted to make the site transfer more robust and to increase accuracy levels.

• Record URL:
  • https://doi.org/10.1016/j.jcmr.2024.100013
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2950105924000044
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/29501059
• Supplemental Notes:
  • © 2024 The Authors. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Siebert, Felix Wilhelm
  • Riis, Christoffer
  • 0000-0002-4540-6691
  • Janstrup, Kira Hyldekær
  • Lin, Hanhe
  • Kristensen, Jakob
  • Gül, Oguzhan
  • Hüttel, Frederik Boe
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; Photos; References; Tables;
• Pagination: 100013
• Serial:
  • Journal of Cycling and Micromobility Research
  • Volume: 2
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2950-1059
  • Serial URL: https://www.sciencedirect.com/journal/journal-of-cycling-and-micromobility-research

Subject/Index Terms

• TRT Terms: Bicycle helmets; Computer vision; Detection and identification system applications; Helmet use; Machine learning
• Geographic Terms: Copenhagen (Denmark)
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01913903
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 4 2024 4:59PM