Comparative review of advanced seatbelt detection: Infrared sensors vs. image-based systems

Abstract

The seatbelt, often heralded as the first line of defense in vehicular safety, plays an indispensable role in mitigating the risks associated with traffic incidents. Consequently, the push towards enhancing transportation safety has led to the emergence of sophisticated seatbelt detection systems. In this study, we undertake a comprehensive examination of four cutting-edge detection methodologies: RF (Radio Frequency), Infrared Marker Vision, Convolutional Neural Networks (CNN), and the You Only Look Once (YOLO) approach. Each method is dissected to understand its efficacy in determining if a passenger has properly donned their seatbelt. Beyond just immediate detection, this paper also casts a vision towards the horizon of seatbelt detection advancements. We explore how such detection systems might seamlessly integrate with advanced vehicle safety infrastructures, and postulate on their pivotal role in the burgeoning domain of autonomous vehicles. As self-driving cars become an imminent reality, the importance of reliable seatbelt detection mechanisms will only magnify. Thus, through this paper, we not only shed light on current methodologies but also endeavor to chart the trajectory of future innovations in the realm of seatbelt safety detection.