Background The motorcycle is the main form of transport for many road users in the world, especially in low- and middle-income countries (LMIC). and since motorcycle riders are critically vulnerable in case of a crash, there should be strong enforcement of road safety related rules, such as helmet use. However, insufficient resources in LMIC hinder a comprehensive and continuous enforcement, subsequently leading to risky rider behavior. Computer vision can be used as a resource-efficient method to detect safety-critical behavior of motorcyclists and facilitate targeted enforcement on a country wide level.
Methods Using video data from Myanmar, a computer vision algorithm was trained to detect helmet use of motorcycle riders. The algorithm was trained on 91,000 frames of the video dataset, containing 10,006 individual annotated motorcycles. It was then tested on diverse data that was not part of the training dataset, and compared to helmet use rates registered by human observers.
Results The computer vision based approach can detect motorcycle helmet use with a precision of -4.4% to +2.1% compared to human observation, independent of traffic flows and road type. Detection accuracy deteriorated only when video data was unclear, e.g. due to adverse weather conditions.
Conclusion Computer vision can facilitate an economic data collection of crucial road safety related rider behaviors. It can be applied to collect continuous data and create an evidence base for police enforcement that targets specific patterns of traffic law violations.
Learning Outcomes Attendants will learn how computer vision can be levied to improve road safety globally.