Researchers at Technical University Target Risk Management (Detecting motorcycle helmet use with deep learning)

2019 NOV 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Risk Management. According to news reporting originating from Berlin, Germany, by NewsRx correspondents, research stated, “The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic.”

Financial support for this research came from Deutsche Forschungsgemeinschaft.

Our news editors obtained a quote from the research from Technical University, “However, especially in developing countries where the motorcycle is the main form of transportation, there is a lack of comprehensive data on the safety-critical behavioral metric of motorcycle helmet use. This lack of data prohibits targeted enforcement and education campaigns which are crucial for injury prevention. Hence, we have developed an algorithm for the automated registration of motorcycle helmet usage from video data, using a deep learning approach. Based on 91,000 annotated frames of video data, collected at multiple observation sites in 7 cities across the country of Myanmar, we trained our algorithm to detect active motorcycles, the number and position of riders on the motorcycle, as well as their helmet use. An analysis of the algorithm’s accuracy on an annotated test data set, and a comparison to available human-registered helmet use data reveals a high accuracy of our approach. Our algorithm registers motorcycle helmet use rates with an accuracy of -4.4% and +2.1% in comparison to a human observer, with minimal training for individual observation sites. Without observation site specific training, the accuracy of helmet use detection decreases slightly, depending on a number of factors. Our approach can be implemented in existing roadside traffic surveillance infrastructure and can facilitate targeted data-driven injury prevention campaigns with real-time speed.”

According to the news editors, the research concluded: “Implications of the proposed method, as well as measures that can further improve detection accuracy are discussed.”

For more information on this research see: Detecting motorcycle helmet use with deep learning. Accident Analysis & Prevention, 2019;134():105319. Accident Analysis & Prevention can be contacted at: Pergamon-Elsevier Science LTD, the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

The news editors report that additional information may be obtained by contacting F.W. Siebert, Dept. of Psychology and Ergonomics, Technical University, Marchstraße 12, 10587 Berlin, Germany.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2019.105319. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal Accident Analysis & Prevention is: Pergamon-Elsevier Science LTD, the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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