## 4 Experiments and Analysis
### Model Comparison Experiment  
#### 4.4.1 Comparison of Detection Accuracy Across Models  
To evaluate the performance of different detection models on the SfdDD and YawDD-E datasets, we compared a variety of models, including Faster R-CNN [44], SSD [26], YOLOv3-tiny [27], YOLOv5n [28], YOLOv6n [29], YOLOv7-tiny [30], YOLOv8n [31], YOLOv9-C [32], YOLO-SGC [15], YOLOv10n [33], YOLOv11n [34], and YOLOv12n [35]. We also compare other computer vision-based dangerous driving behavior detection models proposed by researchers. The results of the experiments are displayed in Tables 2 and 3. Our model outperforms the other 12 detection models on both the YawDD-E public dataset (for frontal-view detection) and the SfdDD public dataset (for side-view detection). Specifically, our model achieves the highest [EMAIL_ADDRESS] across all models, with a 0.4-75.9% improvement in [EMAIL_ADDRESS] on YawDD-E and a 0.6-49.2% improvement on SfdDD. In addition, YOLO-AFR excels in other metrics as well. On the SfdDD dataset, YOLO-AFR improves the [EMAIL_ADDRESS]:0.95 by 0.1 over the previous SOTA model, YOLO-SGC, and achieves the highest precision among all models, significantly outperforming the others. On the YawDD-E dataset, YOLO-AFR achieves the highest recall and F1 score, demonstrating its robust capability in dangerous driving behavior detection tasks. From Tables 4 and 5, it is evident that our model outperforms 14 other recently proposed dangerous driving behavior detection models on both the YawDD-E front-view dataset and the SfdDD side-view dataset. Specifically, our model achieves an improvement in the [EMAIL_ADDRESS] metric by 0.41% to 19.3% on YawDD-E and by 0.92% to 5.77% on SfdDD, surpassing other models and highlighting its exceptional accuracy in detecting dangerous driving behaviors. Furthermore, while the models discussed in the related literature are unable to simultaneously detect dangerous driving behaviors from both frontal and side views, our model excels in handling both perspectives, demonstrating remarkable adaptability and versatility. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **Precision** & **Recall** & **F1-Score** \\ \hline Faster R-CNN & 0.557 & 0.352 & 0.541 & 0.601 & 0.569 \\ SSD & 0.879 & 0.507 & 0.865 & 0.842 & 0.853 \\ YOLOv3-tiny & 0.935 & 0.698 & 0.884 & 0.864 & 0.874 \\ YOLOv5n & 0.934 & 0.706 & 0.892 & 0.869 & 0.880 \\ YOLOv6n & 0.950 & 0.734 & 0.895 & 0.891 & 0.893 \\ YOLOv7-tiny & 0.934 & 0.728 & 0.923 & 0.863 & 0.892 \\ YOLOv8n & 0.952 & 0.764 & 0.933 & 0.879 & 0.905 \\ YOLOv9-C & 0.962 & 0.772 & 0.945 & 0.890 & 0.917 \\ YOLO-SGC & 0.972 & **0.793** & **0.949** & 0.908 & 0.928 \\ YOLOv10n & 0.960 & 0.738 & 0.900 & 0.943 & 0.920 \\ YOLOv11n & 0.963 & 0.742 & 0.922 & 0.922 & 0.920 \\ YOLOv12n & 0.963 & 0.737 & 0.928 & 0.920 & 0.922 \\ YOLO-AFR & **0.976** & 0.763 & 0.936 & **0.947** & **0.940** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison on the YawDD-E dataset. The top results for each metric are shown in bold, with the next best results underlined. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **Precision** & **Recall** & **F1-Score** \\ \hline Faster R-CNN & 0.663 & 0.512 & 0.661 & 0.666 & 0.663 \\ SSD & 0.933 & 0.551 & 0.937 & 0.942 & 0.939 \\ YOLOv3-tiny & 0.958 & 0.560 & 0.955 & 0.957 & 0.956 \\ YOLOv5n & 0.954 & 0.543 & 0.943 & 0.940 & 0.941 \\ YOLOv6n & 0.964 & 0.563 & 0.964 & 0.944 & 0.951 \\ YOLOv7-tiny & 0.968 & 0.548 & 0.967 & 0.968 & 0.967 \\ YOLOv8n & 0.970 & 0.586 & 0.958 & 0.962 & 0.960 \\ YOLOv9-C & 0.983 & 0.590 & 0.978 & **0.988** & **0.983** \\ YOLO-SGC & 0.980 & 0.596 & 0.976 & 0.982 & 0.979 \\ YOLOv10n & 0.966 & 0.586 & 0.947 & 0.953 & 0.950 \\ YOLOv11n & 0.972 & 0.590 & 0.961 & 0.961 & 0.961 \\ YOLOv12n & 0.971 & 0.575 & 0.950 & 0.960 & 0.954 \\ YOLO-AFR & **0.989** & **0.698** & **0.978** & 0.982 & 0.980 \\ \hline \hline \end{tabular}
\end{table}