## 6 Conclusions  
In this paper, we propose SBD-Net, a lightweight and efficient model for detecting students' behaviors in classroom environments. By leveraging advanced computer vision techniques, including the FocalModulation module for multi-level feature fusion, the ESLoss loss function for addressing sample imbalance, and the Dyhead structure to incorporate multiple attention mechanisms without increasing computational complexity, SBD-Net excels in the real-time monitoring and analysis of students' behaviors. These innovations enable teachers to adapt their instructional strategies more effectively, improving the overall quality of education.  
Our experimental results on the SCBehavior and SCB-U datasets demonstrate the superiority of SBD-Net. The model achieved a mAP of 0.824 on SCBehavior, outperforming the baseline model YOLOv8 by 4.3%, while maintaining a low computational complexity of 9.8 GFLOPs. In addition, it demonstrated a 3.8% improvement in recall, highlighting its ability to handle unevenly distributed behaviors and perform high-precision detection in complex classroom scenarios. SBD-Net also showed strong performance on the newly introduced SCB-U dataset, validating its generalizability to different classroom environments.  
However, SBD-Net has some limitations. It is primarily designed for specific classroom settings, which may limit its adaptability to varied educational contexts. Additionally, while its computational demands have been reduced, further optimization is needed for its deployment on low-power devices. Moreover, the current focus on overt behaviors overlooks subtler psychological and emotional states, which require further exploration.  
Future research will focus on optimizing the model's architecture for enhanced accuracy and efficiency. Expanding the dataset to cover a wider range of classroom scenarios, age groups, and cultural backgrounds will further enhance SBD-Net's generalizability. Additionally, exploring multimodal data fusion by integrating visual, audio, and textual inputs will provide a more holistic analysis of student behaviors and emotional states. Techniques such as model compression, quantization, and edge computing will be explored to enable efficient deployment on resource-constrained devices. Collaborating with educators to refine behavior classification and ensure practical applicability will be a key focus, ultimately contributing to the development of intelligent educational systems and the widespread adoption of smart classrooms.  
Conceptualization, Z.W. and C.Z.; methodology, Z.W.; software, Z.W. and C.Z.; validation, Z.W., M.W., L.L., and C.Z.; formal analysis, Z.W.; investigation, Z.W., M.W., L.L., and C.Z.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, Z.W. and L.L.; writing--review and editing, Z.W. and M.W.; visualization, Z.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research work in this paper was supported by the National Natural Science Foundation of China (No. 62177022, 61901165, 61501199), Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE (No. CCNU24 JC033), AI and Faculty Empowerment Pilot Project (No. CCNUAI&FE2022-03-01), Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE and Hubei Province (No. xtzd2021-005), and Natural Science Foundation of Hubei Province (No. 2022 CFA007).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.