使用深度学习检测摩托车头盔的使用
Detecting motorcycle helmet use with deep learning
摘要：
交通的持续机动化导致全球道路相关伤亡人数持续增加。为了解决这个问题，各国政府正在集中精力在交通中实施安全和守法的行为。然而，特别是在摩托车是主要交通工具的发展中国家，缺乏关于摩托车头盔使用的安全关键行为指标的全面数据。由于缺乏数据，无法开展对预防伤害至关重要的有针对性的执法和教育活动。因此，我们已经开发了一种算法，使用深度学习方法从视频数据中自动注册摩托车头盔的使用。基于在缅甸7个城市的多个观察点收集的91，000帧带注释的视频数据，我们训练了我们的算法来检测活跃的摩托车，摩托车上骑手的数量和位置，以及他们的头盔使用情况。对算法在注释测试数据集上的准确性的分析，以及与可用的人类注册头盔使用数据的比较，揭示了我们的方法的高准确性。与人类观察者相比，我们的算法以-4.4%和+2.1%的准确度记录了摩托车头盔使用率，并且对各个观察点进行了最少的培训。如果没有观察现场的具体培训，头盔使用检测的准确性略有下降，这取决于许多因素。我们的方法可以在现有的路边交通监控基础设施中实施，并可以实时促进有针对性的数据驱动的伤害预防活动。所提出的方法的影响，以及可以进一步提高检测精度的措施进行了讨论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
