  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.
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. Implications of the proposed method, as well as
measures that can further improve detection accuracy are discussed.
