## 3 Results and Discussion  
### Model Comparison  
The evaluation of object detection models plays a pivotal role in assessing their real-world performance. In this study, we investigated three variants of the YOLOv7 model: YOLOv7, YOLOv7x, and YOLOv7-tiny, across two diverse datasets: \"ISTraffic\" and COCO. We evaluated the performance of the models based on [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, precision, and recall metrics. Figure 4 demonstrates the results of the performance metrics. YOLOv7 achieved a [EMAIL_ADDRESS] of 0.86, indicating robust performance in detecting objects within the \"ISTraffic\" dataset. The model's precision of 0.89 suggests an effective reduction in false positives, while its recall of 0.83 ensures comprehensive vehicle capture. YOLOv7x outperformed YOLOv7 with a [EMAIL_ADDRESS] of 0.87. Its higher precision (0.89) and recall (0.84) demonstrate improved localization and classification. YOLOv7-tiny achieved a [EMAIL_ADDRESS] of 0.81, slightly lower than the other variants. However, its precision (0.84) and recall (0.77) remain competitive. The \"ISTraffic\" dataset, tailored to traffic surveillance scenarios, appears more amenable to YOLOv7 variants. Conversely, the COCO dataset, with its diverse and complex scenes, posed greater challenges, resulting in lower mAP scores for all variants. YOLOv7 attained a [EMAIL_ADDRESS] of 0.45 on the COCO dataset. While its precision of 0.50 is reasonable, the lower recall (0.52) indicates missed detections. YOLOv7x  
Figure 4: The [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, precision, and recall performance metrics of the models. performed similarly, with a [EMAIL_ADDRESS] of 0.46. Its precision (0.54) and recall (0.49) align closely with YOLOv7. YOLOv7-tiny achieved a [EMAIL_ADDRESS] of 0.36, indicating the need for further enhancements. This suggests that increased model complexity, achieved through additional layers and features, enhances detection performance. On both datasets, YOLOv7x consistently outperformed other variants, exhibiting a 35% performance increase, primarily attributed to the \"ISTraffic\" dataset. YOLOv7-tiny prioritizes computational efficiency over mAP. While its precision remains competitive, the lower recall indicates that some objects may be missed. Several studies have evaluated deep learning models for vehicle detection and recognition, offering insights into their strengths and weaknesses. [PERSON] et al. [45] found that the YOLO model outperformed others, including Faster-RCNN and SSD, in identifying vehicle types. Similarly, [PERSON] et al. [46] proposed YOLO v2 and Faster R-CNN to enhance detection accuracy, noting that YOLO v2 provides faster speeds and lower computational costs, while Faster R-CNN achieves higher accuracy. [PERSON] et al. [47] reported that SSD excelled in performance and processing speed, though Faster R-CNN and YOLO v3 also demonstrated high accuracy and efficiency. [PERSON] et al. [48] highlighted that only YOLO v3 was suitable for real-time detection in surveillance systems due to its low latency. Collectively, these studies contribute valuable perspectives on the performance of various models in vehicle detection tasks. Detection Accuracy of the YOLOv7x-SORT Algorithm Inter-Comparability Under Different Environmental Conditions  
In this study, we conducted experiments using the YOLOv7x and SORT algorithm, which consistently outperformed other variants, for object detection and tracking in real-world scenarios. For this purpose, eight videos were captured under varying environmental conditions, including different times of day, weather conditions, and traffic levels. Figure 5 presents a visual representation of the classes, assigned names, and tracks of the vehicles as observed in the video images obtained from the live broadcast stream. The red color represents 'cars', orange represents'motorcycles', yellow is for'shuttles', and green is for 'buses'.