# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 6 Conclusions Remarks  
The present study investigated the identification of the safety belt status of a vehicle using the YOLOv7 algorithm. The model was constructed utilizing a dataset of 1240 images sourced from Cape Town as well as crowdsourcing and random online image acquisition methods. The dataset was divided into training, testing, and validation sets with a ratio of 80:10:10 using a Python script to automate the partitioning procedure. During the process of training the model, the findings of the present study exhibited a higher level of effectiveness in comparison to other relevant studies that were reviewed. The study under consideration successfully attained a mean average precision (mAP) of 99.6% at a threshold of 0.50. Additionally, it achieved an F1 score of 98% and precision rates of 99.7% and 99.5% for the buckled and unbuckled classes, respectively, within the model. Following a successful evaluation, the model was effectively deployed onto the Jetson Nano development board for the purpose of conducting onboard experiments. The experiments conducted revealed that the model operated in accordance with the proposed design concept, which involved utilizing a pillar loop as the key factor for distinguishing between the buckled and unbuckled states of a safety belt. Additional observations were made regarding the model's ability to detect various challenging scenarios. These included the detection of blurry scenes, partially occluded environments, instances where occupants were seated with their seatbelts fastened behind their backs, and scenes with low-lighting conditions. Nevertheless, it was observed that the model exhibited limitations in detecting instances of missing safety belt features under conditions of high light intensity. In contrast to the current safety belt monitoring system, which can be easily deceived by methods such as fastening the safety belt behind the back or utilizing a safety belt stopper to mimic the latch plate of a safety belt, our proposed model employs a camera as the sensing mechanism. This model is capable of accurately distinguishing between the states of being buckled and unbuckled, even in situations where vehicle occupants intentionally fasten their belts behind their backs. The study under consideration exhibited the greatest level of precision in comparison to the most recent studies put forth in the existing body of research. Nevertheless, there remains potential for further enhancement. In order to advance the field, it is recommended that future studies place greater emphasis on improving the ability of models to accurately detect the presence of safety belts in situations characterized by high levels of illumination. One possible approach to resolving the challenges related to lighting conditions is to explore recent iterations of the YOLO algorithms, which have demonstrated superior levels of precision and resilience in comparison to alternative object detection algorithms employed in comparable research endeavours. These algorithms have the potential to be integrated with advanced camera technologies that possess the capability to adjust and adapt to different lighting conditions. **Author Contributions:** Conceptualization, M.S. ; methodology, L.N. ; software, L.N. ; validation, M.S. ; formal analysis, L.N. ; investigation, L.N. ; resources, E.D.M. ; data curation, L.N. ; writing--original draft preparation, L.N. ; writing--review and editing, L.N. ; visualization, L.N. ; supervision, M.S. and E.D.M. ; project administration, E.D.M. ; funding acquisition, M.S. and E.D.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding except for the publication funding provided by the Central University of Technology. **Data Availability Statement:** The data are available on request when a reasonable amount of time is given.