Automated Detection of Motorcycle Helmet Use
| Autor: | Hasan Merali, Orla Murphy, Devika Singh, Paul McNicholas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Road Safety, Vol 33, Iss 3 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2652-4252 2652-4260 |
| DOI: | 10.33492/JRS-D-22-00010 |
| Popis: | Road traffic collisions are among the top ten causes of death worldwide with more than 1.3 million deaths annually (WHO, 2018). Riders of motorised two- and three-wheelers are more vulnerable to injury and death and make up 28% of global road traffic deaths. In some regions, such as South-East Asia, this number is as high as 43% (WHO, 2018). Correct helmet use reduces the risk of death by 42% and the risk of head injuries by 62% (Liu, Ivers, Blows, Lo, & Norton, 2008). Increasing motorcycle helmet usage to close to 100% by 2030 has been identified as one of the twelve road safety targets by the Global Road Safety Partnership (WHO, 2018). Despite the clear benefits of wearing a helmet, increasing helmet use is challenging especially in low- and middle-income countries (LMICs). A large-scale helmet use media campaign in Thailand over five years showed no benefit (Patummasut, Phewchean, & Sirirattanapa, 2019). While legislating helmet use has shown a clear benefit, there is a disparity between the legislative benefit in high-income countries (HICs) compared to LMICs, with LMICs showing lower use of helmets and less reduction in brain injuries (Lepard, Spagiari, & Park, 2021). |
| Databáze: | Directory of Open Access Journals |
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