## 4 Experiments and Analysis
### Ablation Experiment
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **SEAM** & **FRFN** & **SC-Conv** & **Category** & **Precision** & **Recall** & **F1-Score** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline \multirow{2}{*}{None} & & & \multicolumn{2}{c}{Yawn} & 0.877 & 0.977 & 0.924 & 0.975 & 0.809 & \multirow{2}{*}{0.963} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.926 & 0.867 & 0.896 & 0.954 & 0.770 & 0.963 \\  & & & \multicolumn{2}{c}{Playing phone} & **0.982** & 0.916 & 0.948 & 0.961 & 0.632 & \\ \hline \multirow{2}{*}{A} & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.868 & 0.971 & 0.917 & 0.975 & 0.819 & \multirow{2}{*}{0.973} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.927 & 0.913 & 0.920 & 0.962 & 0.775 & 0.973 \\  & & & \multicolumn{2}{c}{Playing phone} & 0.955 & 0.951 & 0.953 & 0.981 & 0.667 & \\ \hline \multirow{2}{*}{B} & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.836 & **0.994** & 0.908 & 0.977 & 0.809 & \multirow{2}{*}{0.971} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.930 & 0.927 & **0.929** & 0.957 & 0.784 & 0.969 \\  & & & \multicolumn{2}{c}{Playing phone} & 0.956 & 0.944 & 0.950 & 0.973 & 0.657 & \\ \hline \multirow{2}{*}{B} & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & **0.904** & 0.966 & **0.934** & 0.977 & 0.826 & \multirow{2}{*}{0.970} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.944 & 0.874 & 0.908 & 0.965 & 0.769 & 0.970 \\  & & & \multicolumn{2}{c}{Playing phone} & 0.977 & 0.895 & 0.934 & 0.969 & 0.636 & \\ \hline \multirow{2}{*}{A} + B & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.882 & 0.986 & 0.931 & 0.977 & 0.823 & \multirow{2}{*}{0.971} \\  & & & \multicolumn{2}{\"Drinking\" category, increasing Precision from 0.930 to 0.979 and improving [EMAIL_ADDRESS] to 0.984. This demonstrates SEAM's effectiveness in capturing temporal contextual information. The FRFN module shows improvements in overall [EMAIL_ADDRESS]:0.95 scores, particularly for the \"Drinking\" category as well, where [EMAIL_ADDRESS]:0.95 reaches 0.691, validating its strength in multiscale feature fusion. The SC-Conv module enhances the model's focus on spatially relevant regions, with overall [EMAIL_ADDRESS] achieving 0.979, indicating its contribution to spatial localization accuracy. When any two modules are combined (A + B, A + C, or B + C), further performance gains are observed due to their complementary effects. For example, the combinations A+B and B+C reach [EMAIL_ADDRESS] scores of 0.977 and 0.980, respectively, outperforming the single-module configurations. The complete integration of all three modules is our proposed YOLO-AFR model (A + B + C), which achieves the best results on the SfdDD dataset, with [EMAIL_ADDRESS] increasing to 0.989, a 1.8% improvement over the baseline YOLOv12. Notably, in the challenging 'Playing phone' and 'Drinking' categories, [EMAIL_ADDRESS]:0.95 reaches 0.600 and 0.695, demonstrating robust detection capability for fine-grained behaviors and small targets. A similar trend is observed in the YawDD-E dataset. Each individual module contributes positively, with SC-Conv particularly effective in the \"Yawn\" category, raising the F1-Score to 0.934 and [EMAIL_ADDRESS]:0.95 to 0.826. The FRFN module enhances recall and overall [EMAIL_ADDRESS] in \"Closed eyes\", while SEAM consistently improves [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:95 across all categories, confirming its strength in modeling temporal consistency. When all three modules are combined, the YOLO-AFR model achieves a [EMAIL_ADDRESS] of 0.976, surpassing the baseline by 1.3%, respectively, while maintaining balanced performance across categories. These results highlight the model's strong generalization ability and robustness under various viewpoints. Figure 8 illustrates the enhancement of [EMAIL_ADDRESS] gains on both the SfdDD and YawDD-E datasets as key components are integrated. Figure 9 illustrates the synergistic interaction of the FRFN, SC-Conv, and SEAM modules, pivotal to YOLO-AFR's enhanced performance.