## 5 Experimental Results and Analysis
### Performance
#### 5.5.3 Ablation Study  
After conducting several tests, we obtained the ablation experiment results shown in Table 5 above. Compared with the benchmark model YOLOv8n, our method shows significant improvements in various metrics. First, the base YOLOv8n model, without any modifications, achieved a precision of 0.799, recall of 0.725, mAP@0.5 of 0.781, and mAP@0.5-0.95 of 0.595, with 30 M parameters and a computational complexity of 8.9 G FLOPs. When we introduced the Focal Modulation (FM) module, replacing the SPPF module in YOLOv8, the mAP@0.5 increased by 1.8% to 0.799, and the mAP@0.5-0.95 improved by 0.8% to 0.603. The parameter count increased to 31.8 M, and the computational complexity reduced to 8.2 G FLOPs. This indicates that the Focal Modulation module enhances recall and detection performance while maintaining a lower computational complexity. Further, we incorporated the Dyhead detection head, which combines multiple attention mechanisms to enhance the detection capability of complex behaviors. With Dyhead, the precision reached 0.783, a decrease of 1.6% compared to YOLOv8n, but the recall increased by 1.2% to 0.737. The mAP@0.5 was 0.808, an increase of 2.7%, and mAP@0.5-0.95 slightly increased by 0.4% to 0.599. The parameter count rose to 36.5 M, and the computational  
Figure 8: Performance on SCB-U dataset. (The Chinese text in the top left corner represents the recording time, while the text in the bottom right corner represents the recording location). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP@0.5 (\%)** & **mAP@0.5-0.95 (\%)** & **Params (MB)** & **Flop (G)** \\ \hline YOLOv8n & 0.799 & 0.725 & 0.781 & 0.595 & 30 M & 8.9 \\ v8n + FM & 0.792 & 0.731 & 0.799 & 0.603 & 31.8 M & 8.2 \\ v8n + FM + Dyhead & 0.783 & 0.737 & 0.808 & 0.599 & 36.5 M & 9.6 \\ Our Method & **0.804** & **0.763** & **0.824** & **0.619** & 36.5 M & 9.8 \\ \hline \hline \end{tabular} Bold indicates the best performance.  
\end{table} 
Table 5: Ablation experiment. complexity was 9.6 G FLOPs. The inclusion of the Dyhead detection head significantly improved the model's behavior detection performance in complex environments. Finally, in our complete method, in addition to the aforementioned improvements, we optimized the classification loss function by adopting the improved ESLoss function, as shown in Figure 9. The optimized model achieved the best results: precision increased by 0.5% to 0.804, recall increased by 3.8% to 0.763, mAP@0.5 improved by 4.3% to 0.824, and mAP@0.5-0.95 improved by 2.4% to 0.619. Although the parameter count increased to 36.5 M and the computational complexity was 9.8 G FLOPs, the overall performance of the model improved significantly. These improvements validate the importance of each module in enhancing the model's performance. In particular, the optimization of the classification loss function greatly enhanced the model's precision and recall, proving the effectiveness of our method. We used a confusion matrix to demonstrate that SBD-Net effectively detects various student behaviors with high accuracy in key categories, as shown in Figure 10. The model excels in recognizing 'write', 'lookup', and 'discuss' behaviors, which are critical for understanding student engagement and classroom dynamics. These high accuracies highlight the model's robustness in identifying distinct and prominent classroom activities. However, there are areas for improvement, particularly in distinguishing'read', 'turn_head', and'stand' behaviors from similar actions. The moderate performance in these categories suggests that the model faces challenges in differentiating these behaviors due to their visual similarities with other actions, such as 'lookup' and 'raise_hand'. Enhancing feature extraction and incorporating more training samples for these specific behaviors could help in reducing the confusion and improving the overall detection accuracy.