## 5 Experimental Results and Analysis
### Baselines  
To validate the effectiveness of our model, we compare it with several baseline models. These baseline models include a number of detection algorithms that are widely recognized in academia and industry, and this comparative analysis allows us to comprehensively assess the performance of our model in different environments and tasks to ensure its usefulness and reliability in student behavior recognition applications. YOLOv5 [17]: YOLOv5 is the fifth iteration of the YOLO (You Only Look Once) family, known for its real-time object detection capabilities. It is designed to perform both detection and classification in a single pass through the network, making it highly efficient. YOLOv5 improves upon its predecessors with better accuracy, speed, and a more modular architecture. YOLOv5 uses CSP-Darknet53 as its backbone, which integrates Cross Stage Partial (CSP) connections to improve gradient flow and reduce computational cost. And it also employs a Path Aggregation Network (PANet) in the neck, which enhances information flow between layers and helps in detecting objects at various scales. It offers several variants like YOLOv5s (small), YOLOv5m (medium), YOLOv5l (large), and YOLOv5x (extra-large), allowing users to choose based on their specific needs and computational resources. In our study, we chose YOLOv5s as one of the baseline models because YOLOv5-s is a smaller and more efficient variant of the YOLOv5 model family, designed for real-time target detection, with a similar number of parameters and computational effort to our proposed model, which allows for a better comparison. YOLOv7 [45]: YOLOv7 introduces several architectural innovations and enhancements that improve its performance compared to its predecessors. Developed by the original creators of YOLO, it focuses on maintaining a balance between speed and detection accuracy, making it suitable for a wide range of applications. First, it introduces ELAN to improve the network's ability to learn complex features by integrating more efficient feature aggregation strategies. And YOLOv7 also integrates RepVGG blocks, which streamline the network architecture, reduce memory usage, and improve inference speed without sacrificing accuracy. We also chose the yolov7-tiny version, which has a similar amount of parameters to our model, as one of the baseline models. SSD [32]: SSD is a powerful and efficient object detection model that strikes a good balance between speed and accuracy. Its one-shot detection method, use of multi-scale feature maps, and real-time performance make it a popular choice for many real-worldapplications, so we chose it as the baseline model to compare with ours, and despite some challenges in achieving the highest accuracy for small or dense objects, it is still a fairly important representative model. Faster R-CNN [31]: Faster R-CNN consists of a two-stage approach where the first stage generates region proposals using a Region Proposal Network, and the second stage classifies these proposals and refines their bounding boxes. This architecture enables Faster R-CNN to achieve a high detection accuracy, particularly in complex scenes. In the context of classroom student behavior recognition, Faster R-CNN was selected as one of the baseline models for comparison due to its proven effectiveness in detecting and localizing objects accurately. Deformable-DETR [46]: Deformable-DETR is an advanced object detection model that builds upon the original DETR framework by incorporating deformable attention mechanisms. It addresses some of the limitations of standard DETR, such as slow convergence and difficulties in handling small objects, by enabling more efficient and flexible feature extraction. In the context of classroom student behavior recognition, Deformable-DETR is chosen as one of the baseline models due to its superior capability to capture detailed and varied student actions and postures.