Innovative Approaches for Automated Seat Belt Compliance Detection

Abstract

This research delves into the significance of seat belt utilization, particularly within public transportation, for both drivers and passengers. The article examines the issue of non-compliance with seat belt regulations, including the use of counterfeit manufacturer items to disable seat belt warnings and improper seat belt positioning. With a focus on advanced technology, the article proposes various technical approaches to enhance the automatic detection of seat belt usage. These suggested methods entail the integration of a camera and buzzer with a microcontroller of seat belt compliance detection and the development of a supervised machine learning model that achieved a 99% accuracy rate in identifying seat belt usage through image analysis. Furthermore, the research introduces an innovative approach to verifying individuals' specific health conditions that health authorities have granted limited exemptions from wearing seat belts. The ultimate objective of this study is to promote seat belt usage, thereby safeguarding both drivers and passengers. It is intended that the findings of this research be considered by the vehicle manufacturing industry.