# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 1 Introduction  
The utilization of a safety belt is highly crucial in the mitigation of severe injuries and the substantial reduction in the probability of fatalities [1; 2; 3; 4]. According to the research conducted by [PERSON] and [PERSON], it has been estimated that a total of 3287 individuals perish on a daily basis as a result of accidents [5]. [PERSON] et al. [6] also submit that the estimated annual number of fatalities resulting from automobile accidents is approximately 1.35 million. This phenomenon persists despite the presence of safety belt monitoring systems in modern vehicles, which serve as reminders for occupants to fasten their seat belts. In South Africa (SA), there exists a specialized branch of law enforcement that is committed to the enforcement of safety belt usage and the enhancement of overall safety measures. However, a significant portion of individuals traveling in vehicles choose not to secure their seatbelts. The subject of safety belt detection is of great significance owing to its capacity to safeguard human lives. At present, there is a scarcity of efficient approaches to guarantee and supervise the proper fastening of safety belts, with the exception of deploying traffic officers on highways [7] and relying on safety belt warning systems that can be easily circumvented by vehicle occupants. The existing methodology is characterized by its laborious and ineffective nature, primarily attributed to the limited number of officers allocated for inspection responsibilities [7]. Additionally, a noteworthy challenge arises from the ability of vehicle occupants to manipulate the safety belt system by either fastening the belt behind their backs or utilizing a device known as a \"seatbelt warning stopper\" to bypass the system [2]. The existing safety belt warning systems demonstrate limited efficacy. At times, motorists observe traffic officers from a considerable distance and subsequently opt to secure their safety belts at that particular juncture. Consequently, there has been an increasing fascination with the automated identification and implementation of safety belt regulations [8]. Scholars have directed their attention towards the development of computer vision algorithms in order to meet the worldwide need of traffic safety agencies for automated safety belt detection solutions that are not solely dependent on onboard sensors. Scholars have addressed the issue of safety belt compliance through two distinct approaches. The first approach involves the installation of cameras along highways to monitor the usage of safety belts [3; 4; 5; 6; 7]. However, the installed cameras may have to consider the fact that there are two modes of computer vision models (single-stage and two-stage mode), and, therefore, the Stage 1 computer vision models are the ones recommended for real-time detection [9; 10]. Other authors, such as [PERSON], [PERSON] et al., and [PERSON] et al., also considered the installation of cameras along highways [11; 12; 13]. The second approach entails the placement of cameras within vehicles to constantly monitor the act of fastening safety belts [2; 14; 15]. Both proposed solutions encounter various challenges. The implementation of safety belt monitoring systems on highways effectively addresses the issue of limited law enforcement personnel, yet it fails to address the underlying concern that drivers may selectively comply with safety belt regulations in areas where they are aware of the presence of such surveillance cameras. The potential exists for the onboard monitoring system to effectively enforce the utilization of a car safety belt. Nevertheless, the process of onboard monitoring encounters the persistent challenge of fluctuating illumination, the presence of blurry images resulting from uneven road conditions, and the obstruction caused by objects within the vehicle. As previously indicated, the existing design of the safety belt monitoring mechanism is susceptible to manipulation.