- AutorIn
- Felix W. Siebert Department of Technology, Management and Economics Technical University of Denmark
- Christoffer RiisDepartment of Technology, Management and Economics Technical University of Denmark
- Kira H. JanstrupDepartment of Technology, Management and Economics Technical University of Denmark
- Jakob Kristensen
- Oguzhan Gül
- Hanhe Lin
- Frederik B. Hüttel
- Titel
- Automated detection of e-scooter helmet use with deep learning
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-824599
- Konferenz
- International Cycling Safety Conference (ICSC). Dresden, 08.-10. November 2022
- Quellenangabe
- Contributions to the 10th International Cycling Safety Conference 2022 (ICSC2022)
Herausgeber: Prof. Dr. Tibor Petzoldt, Prof. Dr. Regine Gerike, Juliane Anke, Dr. Madlen Ringhand, Bettina Schröter
Erscheinungsort: Dresden
Verlag: Technische Universität Dresden
Erscheinungsjahr: 2022
Seiten: 25-27 - Erstveröffentlichung
- 2022
- DOI
- https://doi.org/10.25368/2022.424
- Abstract (EN)
- E-scooter riders have an increased crash risk compared to cyclists [1 ]. Hospital data finds increasing numbers of injured e-scooter riders, with head injuries as one of the most common injury types [2]. To decrease this high prevalence of head injuries, the use of e-scooter helmets could present a potential countermeasure [3]. Despite this, studies show a generally low rate of helmet use rates in countries without mandatory helmet use laws [4][5][6]. In countries with mandatory helmet use laws for e-scooter riders, helmet use rates are higher, but generally remain lower than bicycle use rates [7]. As the helmet use rate is a central factor for the safety of e-scooter riders in case of a crash and a key performance indicator in the European Commission's Road Safety Policy Framework 2021-2030 [8], efficient e-Scooter helmet use data collection methods are needed. However, currently, human observers are used to register e-scooter helmet use either in direct roadside observations or in indirect video-based observation, which is time-consuming and costly. In this study, a deep learning-based method for the automated detection of e-scooter helmet use in video data was developed and tested, with the aim to provide an efficient data collection tool for road safety researchers and practitioners.
- Freie Schlagwörter (DE)
- E-Scooter-Sicherheit, Verletzungsprävention, Helme, Deep Learning, Computer Vision
- Freie Schlagwörter (EN)
- e-Cooter safety, injury prevention, helmets, deep learning, computer vision
- Publizierende Institution
- Technische Universität Dresden, “Friedrich List” Faculty of Transport and Traffic Sciences, Institute of Transport Planning and Road Traffic
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-824599
- Veröffentlichungsdatum Qucosa
- 19.12.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
- CC BY 4.0