| Autor: |
Siebert, Felix W., Riis, Christoffer, Janstrup, Kira H., Kristensen, Jakob, Gül, Oguzhan, Lin, Hanhe, Hüttel, Frederik B. |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Text<br />Conference Material |
| Popis: |
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. |
| Databáze: |
Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
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