## 2 Literature Review
[2018] improved YOLOv4 to detect students who sleep or use a smartphone by enhancing a feature extraction network that combined top-down and bottom-up paths. The original YOLOv4 was compared with an improved network (PANet and Faster R-CNN), and the results showed that the enhanced network can achieve better results and is suitable for student detection and recognition tasks. [PERSON] et al. [24] presented a classroom behavior detection algorithm based on an improved YOLOv5 object detection model. They combined a weighted bidirectional feature pyramid network (BiFPN) with the feature pyramid network and the path aggregation network (FPN+PAN) structure of YOLOv5. This allowed them to extract the fine-grained features of various behaviors from different scales of the object. They also added a CBAM to the neck network to improve the detection accuracy. Finally, they improved the original non-maximum suppression algorithm using a distance-based intersection ratio to enhance the discrimination of occluded objects. [PERSON] et al. [25] presented a student classroom behavior detection method based on an improved YOLOv7 to address the low accuracy of the existing methods. To improve the detection accuracy in crowded scenes, they integrated the biformer attention module and Wise-IoU into the YOLOv7 network. Their method achieved a [EMAIL_ADDRESS] of 79% using the SCB dataset, which includes 18.4 k of labels and 4.2 k of images covering three behaviors: hand-raising, reading, and writing. This represents a 1.8% improvement over previous results. [PERSON] et al. [26] proposed a novel student classroom behavior detection system that combines deformable DETR with a Swin Transformer and a lightweight Feature Pyramid Network (FPN). To address the limitations of CNN-based target detection methods, their system employs a feature pyramid structure and the CARAFE lightweight operator to process multi-scale feature maps effectively and enhance the network's detection accuracy. This system achieved a significant 6.1% improvement in detection accuracy compared with the original deformable DETR model and outperforms Faster R-CNN, SSD, and YOLOv3 [21], v5 [24], and v7 [25]. According to recent papers, the majority of research has focused on improving accuracy through feature extraction, model combining, or both. For instance, ResNet-101 has been integrated with a Faster R-CNN network to detect student behavior [15]. In addition, the spatial affine transformation network has been combined with CNNs for feature extraction and the detection of student behavior [16]. ResNet50 and CoAtNet have been employed to create a bimodal network for feature extraction [17]. CNN-10, a 10-layer deep CNN, has been used for feature extraction to recognize student behaviors [18], while the OpenPose framework has been utilized to focus on key information [19; 20]. YOLOv3 uses multiple convolutional layers to isolate important features [21], and CBAM has been incorporated into YOLOv3 for feature extraction [22]. For enhanced accuracy, a weighted BiFPN combined with FPN+PAN has been used for feature extraction [23], and a CBAM has been added to it [24]. In YOLOv7, the biformer attention module and Wise-IoU have been applied for feature extraction [25]. To enhance feature extraction, the deformable DETR, Swin Transformer, FPN, and the CARAFE operator have been combined [26]. The use of integrated models and combination methods for feature extraction is crucial to improving the accuracy of student behavior detection and classification tasks. Previous diverse research has demonstrated that a combination of sophisticated feature extraction techniques and model integration strategies can enhance the accuracy of student behavior detection and classification. Our comprehensive analysis of existing approaches found that no single algorithm or model can consistently achieve the highest levels of exactness across all aspects of student behavior analysis.