Design and Development of Automatic Detection System for Motercyclists without Helmet using Machine Learning

Abstract

The use of two-wheelers as a mode of transport is increasing rapidly, but unfortunately, many riders neglect to wear helmets, which can lead to accidents and fatalities. To address this issue, many countries have implemented laws mandating the use of helmets for two-wheeler riders, and the police force often discourages this behavior by issuing traffic violation tickets. However, the process of issuing these tickets is often manual and tedious, which can lead to delays and errors. To solve this problem, a proposed system is to automate the process of detecting riders who are not wearing helmets. The system would use image processing algorithms to extract the license plate number of the rider, which would then be used to issue a traffic violation ticket. The image processing algorithm would consist of five parts, including image procurement, preliminary processing, fringe detection and segmentation, feature extraction, and recognition of character number plates using suitable machine learning algorithms. This automated system would not only make the process of issuing traffic violation tickets faster and more efficient, but it would also increase the compliance of two wheeler riders with helmet laws. This would lead to a reduction in accidents and fatalities caused by not wearing helmets, ultimately making roads safer for everyone