The rapid growth in the commute and vehicles has made exponential growth in the progress of mankind. This growth besides its positive aspects comes with a concern of saving life on road due to accidents. And, hence the technological advancements in the field of machine learning are required to cope up with the challenges such as road safety and traffic rule violations. According to the survey the majority of the life lost in road accidents is due to the negligence of wearing a helmet on a two wheeler vehicle. The enforcement of the traffic rules regarding this violation proves to be a challenge due to dense population and low rate of detection which is primarily caused by the lack of an automated system to detect the violation and take the necessary action. The growing population and the growing number of vehicles cause the manual systems in place to fail in curbing the issue. The recent advancements in Deep Learning and Image Processing provide an opportunity to solve this problem. This manuscript presents the implementation of a system which detects three objects namely the vehicle, non-usage of a helmet and the number plate of the vehicle under consideration using Tensorflow. Deep learning using the SSD MobileNet V2 is the primary technique used to implement the system. The system has been tested under different use cases with successful results.

中文翻译：

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道路安全头盔检测的深度学习方法

通勤和车辆的迅速增长使人类的进步成倍增长。这种增长除其积极方面外，还涉及因事故而挽救道路生命的担忧。并且，因此需要机器学习领域的技术进步来应对诸如道路安全和违反交通规则的挑战。根据调查，在道路交通事故中丧生的大多数是由于在两轮车上戴头盔造成的疏忽。由于人口密集和检测率低，有关这种违规行为的交通规则的执行被证明是一个挑战，这主要是由于缺乏自动系统来检测违规行为并采取必要的措施所致。人口的增长和车辆数量的增加导致现有的手动系统无法解决问题。深度学习和图像处理方面的最新进展为解决此问题提供了机会。该手稿介绍了一个系统的实现，该系统使用Tensorflow检测三个物体，即车辆，未使用的头盔和所考虑的车辆的车牌。使用SSD MobileNet V2进行深度学习是用于实现系统的主要技术。该系统已在不同用例下进行了测试，并取得了成功的结果。未使用Tensorflow使用头盔和正在考虑的车辆的车牌。使用SSD MobileNet V2进行深度学习是用于实现系统的主要技术。该系统已在不同用例下进行了测试，并取得了成功的结果。未使用Tensorflow使用头盔和正在考虑的车辆的车牌。使用SSD MobileNet V2进行深度学习是用于实现系统的主要技术。该系统已在不同用例下进行了测试，并取得了成功的结果。