## 3 Experiment and Results
### Training Results and Analysis  
In this study, the accuracy of two models was compared: YOLOv5n and Improved-YOLOv5. The baseline model for comparison was the YOLOv5n because it has the same depth and width structure as the Improved-YOLOv5. To avoid overfitting, the YOLOv5n and Improved-YOLOv5 models were trained for 1000 epochs with early stopping. Early stopping is a regularization technique that halts training if the model's performance on a validation set stops improving after a certain number of epochs, called the \"patience\". The patience was set to 100 epochs in this case. Both models' performance on the validation set was monitored during training. When the models failed to improve their validation performance after 100 epochs, the training was halted to avoid overfitting. The Improved-YOLOv5, on the other hand, was stopped earlier than YOLOv5n, at epoch 816 versus epoch 517, indicating that it was better able to learn and generalize the data features. The ability of the Improved-YOLOv5 to continue training for a longer period of time suggests that it was less vulnerable to overfitting than YOLOv5n. This could be because the Improved-YOLOv5 had a better architecture or a more appropriate hyperparameter configuration, which resulted in better model generalization. Ultimately, the improved ability of the Improved-YOLOv5 to generalize its features and avoid overfitting may have led to the better performance compared to YOLOv5n. Comparison of the detection results of the different models is shown in Figure 8, which contains a visual comparison of the detection results obtained by the two models, YOLOv5n and Improved-YOLOv5. The figure shows how the two models performed in terms of detecting buses in images, and it can provide a clear comparison of their accuracy. The results in Figure 9 provide insights into how the models learned over time and whether there were any particular trends or patterns in their performance. Additionally, Figure 10 shows a comparison of the models' accuracy. When the two models were compared, the Improved-YOLOv5 outperformed the YOLOv5n in terms of the precision and [EMAIL_ADDRESS]. The precision of the Improved-YOLOv5 model was 0.939, while the precision of the YOLOv5n model was 0.921. This suggests that the Improved-YOLOv5 was better at identifying true positives, which is important for people with visual impairment because false positives can lead to missed buses, frustration, and potential safety hazards. Both models had high recall, indicating that they correctly identified the majority of the buses in the image. The recall for the YOLOv5n was 0.899, while the recall for the Improved-YOLOv5 was 0.895. This demonstrates that both models detected the majority of the buses in the images. The mean Average Precision (mAP) is a metric that measures how well the model performs at various IoU thresholds. Both models in this study had a good [EMAIL_ADDRESS], indicating that they could detect buses with a high level of accuracy. The Improved-YOLOv5, on the other hand, outperformed YOLOv5n, with a [EMAIL_ADDRESS] of 0.933 compared to YOLOv5n's score of 0.923. The Improved-YOLOv5 had a lower [EMAIL_ADDRESS]:0.9 score of 0.619, while the YOLOv5n had a higher score of 0.628. This means that the YOLOv5n detected buses with greater accuracy at various IoU thresholds, especially at higher thresholds, than the Improved-YOLOv5. Figure 8: Comparison of the detection results of different models. (**a**) YOLOv5n model. (**b**) Improved-YOLOv5. In summary, the comparison revealed that the Improved-YOLOv5 outperformed the YOLOv5n in terms of the precision, recall, and [EMAIL_ADDRESS], indicating its ability to detect true positives with greater accuracy, detect the majority of objects in images, and detect objects with a high level of accuracy, respectively. YOLOv5n, on the other hand, had a higher [EMAIL_ADDRESS]:0.9 score, indicating that it was better at detecting buses with greater accuracy at different IoU thresholds, particularly at higher thresholds.