Presenter(s)
Kunal Agrawal
Files
Download Project (1.4 MB)
Description
Motorcycles are one of the most popular modes of transportation, particularly in developing countries such as India, and Vietnam. Due to lesser protection compared to cars and other standard vehicles, motorcycle riders are exposed to a greater risk of crashes. Therefore, wearing helmets for motorcycle riders is mandatory as per traffic rules and automatic detection of motorcyclists without helmets is one of the critical tasks to enforce strict regulatory traffic safety measures. Due to the increase in the number of vehicles on roads, the number of injuries or accidents has also increased. According to a study, approximately 21.5% of motorcycle riders had been wearing helmets at the time of the accident. This has also made a huge impact on city development planning. Due to not wearing a helmet, there’s an exponential increase in the fatality rate during an accident. It has been observed that these fatality rates are higher during the daytime and the dark. To decrease the risk of fatal injuries, we are developing a model that will detect whether a motorcyclist wears a helmet or not. We will be using the traffic data from Vietnam from daytime and nighttime. The trained model using this data will be used further for the city planning simulator.
Publication Date
4-19-2023
Project Designation
Graduate Research
Primary Advisor
Van Nguyen
Primary Advisor's Department
Computer Science
Keywords
Stander Symposium, College of Arts and Sciences
Institutional Learning Goals
Community; Diversity; Scholarship
Recommended Citation
"Detecting Violation of Helmet Rule for Motorcyclists" (2023). Stander Symposium Projects. 3023.
https://ecommons.udayton.edu/stander_posters/3023

Comments
Presentation: 9:40-10:00 a.m., Jessie Hathcock Hall 180