## 3 Experiment and Results
### Training Results and Analysis
According to the findings, the Improved-YOLOv5 could be a better option for bus detection systems because it demonstrated better generalization and less overfitting, resulting in better performance. The difference in the [EMAIL_ADDRESS]:0.9, on the other hand, highlights the need for additional research and testing to determine the best model for the specific application. The comparison of the models' complexity is shown in Table 2. In this analysis, the model complexity comparisons were YOLOv5n and the Improved-YOLOv5, with YOLOv5n serving as the baseline. The goal was to determine which model was more efficient and less complicated, making it a more practical solution for bus detection systems. The number of parameters or the total number of learnable parameters in the model was the first metric considered. YOLOv5n had 1763,224 parameters, while the Improved-YOLOv5 only had 717,020 parameters. This demonstrates that the Improved-YOLOv5 was a simpler model to learn because it had fewer parameters to learn. This is advantageous  
Figure 10: Comparison of the models’ accuracy. Figure 9: Comparison of the models’ accuracy. (**a**) Precision. (**b**) Recall. (**c**) [EMAIL_ADDRESS]. (**d**) [EMAIL_ADDRESS]:0.9. (**e**) Train/box_loss. (**f**) Train/obj_loss. (**g**) Val/box_loss. (**h**) Val/obj_loss. because it means the model is less complex and has a lower risk of overfitting, which is necessary to ensure that the model generalizes well to new data. Furthermore, having fewer parameters means that the model requires less memory and storage, which can be beneficial in resource-constrained environments. The second metric considered was GFLOPs, which measures the number of floating-point operations per second that the model can perform. The Improved-YOLOv5 outperformed the YOLOv5n in this case, with 2.1 GFLOPs versus 4.1 GFLOPs. This means that the Improved-YOLOv5 completed more operations in less time, which can be advantageous in applications where speed is critical, such as real-time object detection. Furthermore, this metric is significant because it determines the model's speed and efficiency, which can have significant implications for real-world applications. The third metric is weight/MB, which measures the model's size in terms of its weight (i.e., the size of the model parameters) per megabyte of storage. In this regard, the Improved-YOLOv5 outperforms the YOLOv5n, with a weight of 1.76 MB versus 3.72 MB. This means that the Improved-YOLOv5 is more compact and takes up less storage space, which is useful in situations where storage space is limited. This metric is also significant because it determines how much storage is required to store the model, which has significant implications for real-world applications. Overall, our analysis of the models' complexity indicates that the Improved-YOLOv5 is a more efficient and less complex model for bus detection systems than YOLOv5n. It has fewer parameters, uses less memory, and can perform more operations in less time. It is also more compact, requiring less storage space. These metrics are significant because they determine how well the model performs in real-world applications as well as the resources needed to run it. As a result, the Improved-YOLOv5 is the more practical solution for bus detection systems because it is simpler, more efficient, and more compact.