# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 5 Discussion  
The primary objective of this study was to assess the efficacy of the YOLOv7 algorithm in detecting the presence of a safety belt. According to Reference [5], the failure to utilize safety belts leads to a significant number of fatalities, with a daily death toll of 3285 and an  
Figure 19: Comparative Analysis of Safety Belt Buckle Detection Performance. Figure 18: The visual distortion observed in the scene is a result of the vehicular vibrations induced by traversing a speed hump at a velocity of 40 km per hour. annual toll of 1.35 million. The investigation of safety belt buckling is a significant area of research due to the potential for grave injuries and fatalities resulting from the act of unbuckling [1; 2; 3; 4]. The majority of research studies conducted thus far have primarily focused on conventional algorithms, which have been found to exhibit instability and inaccuracy when confronted with dynamic environments [15]. Previous approaches have involved the utilization of cameras installed on highways [3; 7; 11; 12; 13] as a means of identifying the status of safety belts. This approach aimed to address the challenge of the limited number of traffic officers available for conducting manual inspection tasks. Nevertheless, this approach anticipates the potential for vehicle occupants to easily fasten their safety belts in regions where the cameras are installed. Previous studies [3; 8] have primarily concentrated on the identification of the driver's utilization of a safety belt, neglecting the safety of other individuals present in the vehicle. Figures 15 and 16 illustrate the efficacy of our model in detecting multiple vehicle occupants, thereby enhancing the safety of a larger number of individuals within the vehicle. Based on the findings of previous studies [3; 12; 15], the majority of research in this field has focused on exploring seat belt buckling solutions through the utilization of conventional algorithms. According to [PERSON] et al. [16], conventional object detection algorithms face difficulties in terms of their speed, accuracy, and adaptability to different environmental conditions. The model under consideration was derived from YOLOv7, which belongs to the same category as other deep-learning-based approaches. Consequently, the study conducted demonstrated a notable level of precision, as evidenced by a mean average precision (mAP) of 99.6%, an F1 score of 98%, and a confidence level of 100% for both the buckled and unbuckled categories. The model exhibited satisfactory performance during the evaluations conducted inside the vehicle on occluded pillar loop scenes, the instances where occupants were attired in clothing resembling the webbing of the safety belt, the scenes with reduced clarity, and the scenarios involving diverse lighting conditions. Experiments 2 through 5 illustrate the experimental outcomes obtained. Nevertheless, the study that was carried out had certain limitations. During the conducted experiments, it was observed that an abundance of sunlight results in the distortion of specific safety belt features, consequently leading to the unsuccessful detection of the safety belt status. Given that the proposed studies rely on the safety belt pillar loop as the primary determinant of the safety belt status, the utilization of a single camera positioned at a fixed angle may result in inaccurate detections. Consequently, the model may erroneously identify occupants as unbuckled, despite the possibility that they are indeed properly secured. The model's training did not include cases involving incorrect buckling of occupants except for when people were buckled behind their backs, leading to a noticeable decrease in confidence when detecting the safety belt status of individuals who are improperly buckled. The model was not evaluated on nocturnal scenes as a result of the absence of a camera capable of supporting night vision capabilities.