## 5 Conclusions and Discussions  
This paper detects students' classroom behavior based on the improved YOLOv5 algorithm and transforms the problem of classroom behavior detection into a fine-grained classification problem of behavior images. By introducing the weighted bidirectional feature pyramid network BiFPN, the multi-scale features of the object are more effectively expressed, mining the fine-grained characteristics of object behavior, reducing the number of missed detections of small target students in the back row of the classroom; the attention mechanism CBAM is integrated into the YOLOv5 algorithm to enhance the saliency of the object area in the complex background, effectively improving the accuracy of the detector; and using DIoU to improve the original non-maximum suppression, further improving the accuracy of the detector, while improving the problem of false detection caused by the problem of classroom occlusion. The experimental results show that the improved algorithm in this paper can accurately detect different classroom behaviors and has higher accuracy than the YOLOv5 algorithm in dense classroom scenes and occlusion scenes, and has better accuracy and robustness. The mAP values of the proposed algorithm are bigger than those values of Faster R-CNN, SSD and the original YOLOv5 algorithm, indicating that the proposed algorithm has a better detection accuracy. The results presented in Figures 15 and 16 also show that the algorithm proposed in this study can be used for detection in dense classroom scenes and occlusion scenes. Moreover, the results show that the proposed algorithm achieves an inference speed of 33 frames per second under GeForce GTX 1080 ti, which meets the requirements of real-time video detection. Although the results are encouraging, the work is limited due to the following reasons. First, students' behaviors often occur coherently and video datasets in real classrooms are difficult to obtain. Second, it is difficult to consider the temporal contextual information of behavior occurrence to increase the accuracy. We did not consider the temporal features before and after each behavior occurrence but focused on the behavior classification problem on single-frame images. Unlike running and jumping that require multiple frames of information, the behaviors performed by students in the classroom often tend to be static and can be judged by single-frame images. The findings of this study have the following potential applications in the classroom scenarios. To monitor students' behavioral status, it is very common to arrange cameras in front of the classroom and connect it to our back-end server. Deploying the algorithm proposed in this study, the way in which students behave can be presented in a timely manner on both the teacher and administration sides. The status of students can be evaluated automatically by classifying their behaviors into positive behaviors (e.g., listening and standing), neutral behaviors (e.g., looking down), and negative behaviors (e.g., lying down). The educational system calculates the proportion of positive, neutral, and negative behaviors by counting the recognized behaviors. If the proportion of negative behaviors exceeds a certain threshold, feedback can be provided to the teacher and suggest he/she improves the teaching strategy of the class. The educational system can also count the number of times that each student's behavior occurs and the period during which positive or negative behavior lasts. In this way, teachers can get more insights on the teaching method that is more appropriate for them at different teaching stages. Despite the performance of the algorithm being improved, the accuracy of student behavior recognition for complex features or state combinations in practical application environments needs to be further investigated. Further work to be undertaken includes the use of contextual information of behaviors for more accurate behavior classification and the accuracy improvement for small target students.