# Student Behavior Recognition in Classroom Based on Deep Learning
## 4 Mechanism of Student Behavior Detection
### Integration of YOLOv5 and CA for Feature Fusion and Extraction  
YOLOv5 is the fifth version of the YOLO series of object detection algorithms, developed by [PERSON] in 2020 [21]. This version inherits the basic concept of the YOLO series of algorithms, which is to extract features from the input image through a neural network and output the category and location of the target at the same time. YOLOv5 was chosen as the foundational architecture for the detection model due to its lightweight structure and high efficiency. Its structure is shown in Figure 3.  
YOLOv5 is widely used in real-time object detection due to its speed, efficiency, and flexibility. However, in scenes with complex backgrounds and significant lighting variations, the model may ignore some important features, leading to reduced detection accuracy. To enhance model accuracy, this paper integrates the CA mechanism into the YOLOv5 framework. The CA mechanism combines spatial and channel attention, enabling the model to more accurately focus on target areas during feature extraction.  
CA [22] stands for Contextual Attention, a mechanism designed to address these challenges by dividing the input sequence into multiple segments and applying self-attention computations to each segment. This approach effectively leverages contextual information within the sequence. As shown in Equation (1), \(x_{1},x_{2}\ldots x_{c}\) represents the input feature tensor, and \(R^{H\times W\times C}\) denotes the dimensions of the feature tensor.  
\[X=[x_{1},x_{2}\ldots x_{c}]\in R^{H\times W\times C} \tag{1}\]  
Figure 3: YOLOv5 network structure.  
By transforming the input, an output tensor of the same size with enhanced representational capacity is produced, as shown in Equation (2).  
\[Y=[y_{1},y_{2}\ldots y_{c}]\in R^{H\times W\times C} \tag{2}\]  
The CA attention mechanism is shown in Figure 4.  
To accurately obtain the width and height information of an image and focus attention, the CA module performs global average pooling on the input feature map in both the width and height directions, thereby generating two feature maps. This method effectively encodes the positional information of the image, thereby enhancing the accuracy of the model. As shown in Equations (3) and (4), given an input \(x_{c}\), two spatial encodings are applied to each channel using pooling kernels \((h,i)\) and \((j,w)\) along the horizontal and vertical axes, respectively.  
\[z_{c}^{h}(h)=\frac{1}{W}\sum_{0\leq i<W}x_{c}(h,i) \tag{3}\]  
\[z_{c}^{w}(w)=\frac{1}{H}\sum_{0\leq j<H}x_{c}(j,w) \tag{4}\]  
Next, the feature maps obtained from the width and height directions are concatenated and processed through a shared \(1\times 1\) convolutional module to reduce their dimensionality. Then, the feature map \(F_{1}\), after batch normalization, is passed through a Sigmoid activation function to generate a new feature map \(f\), as shown in Equation (5).  
\[f=\delta(F_{1}([z^{h},z^{w}])) \tag{5}\]  
Subsequently, along the spatial dimension, the feature map \(f\) is divided into parts \(f^{h}\) and \(f^{w}\). Each part undergoes an up-dimension operation using \(1\times 1\) convolutions, followed by the application of the Sigmoid activation function to generate the final attention vectors \(g^{h}\) and \(g^{w}\).  
\[g^{h}=\delta(F_{h}(f^{h})) \tag{6}\]  
Figure 4: CA attention mechanism.  
\[g^{w}=\delta(F_{w}(f^{w})) \tag{7}\]  
Finally, by applying multiplicative weighting to the original feature map, we obtain the final feature map \(y_{c}(i,j)\) with attention weights along both the width and height dimensions, as shown in Equation (8):  
\[y_{c}(i,j)=x_{c}(i,j)\times g_{c}^{h}(i)\times g_{c}^{w}(j) \tag{8}\]  
This paper integrates an attention module into the backbone of YOLOv5, replacing all convolutional modules with attention module structures. The specific architecture, referred to as YOLOv5-A, is shown in Figure 5.