Helmet Detection using Single Shot Detector (SSD)

Abstract

The higher death risk in motorcycle crashes and construction worker accidents can be due to riders' and employee's inability to wear helmets. To avoid such collisions, it is important to always identify people who are not wearing helmets, and also this model could be of a great help for the motor vehicle department. This paper describes an electronic method for increasingly and simultaneously separating motorbike riders without a helmet from traffic management videos and employees without helmets. The helmet detection problem in this article is solved using a single shot detector model. This model will assess the bounding box region of the motorcycle and the rider with just one unit. After the area has been picked, the proposed model will classify whether the biker is wearing or not wearing a helmet in real-time. The Convolutional Neural Network is used to recognize motorcycles without helmets and to distinguish motorcycles among moving targets.