## 3 Methodologies
### Improved Student Object Detection Algorithm Based on YOLOv8
#### 3.1.2 MCC Module  
YOLOv8 enhances the accuracy and efficiency of the model by replacing the C3 module with the C2f module in the CSPLayer to enhance the feature processing capability  
Figure 1: The network architecture of YOLOv8x-Improved.  
and reduce the computational complexity. Practice shows that adjusting the CSPLayer can effectively improve the network performance. When it comes to finding targets, multi-scale features are very important. Adding null convolution helps the growth of multi-scale pyramid models, which greatly enhance the accuracy of localization. Different sensory fields capture detailed and overall information, and after integration, models that integrate multi-scale pyramid structures perform well in object detection. For this reason, this paper proposes the multi-group cheap convolution (MCC) module as an alternative to the C2f module to handle multi-scale features more efficiently and optimize the computational cost.  
The MCC module is shown in Figure 2 with the following steps:  
1. The input tensor is split into two parts, where x_group occupies half of the channel and x_cheap occupies the other half.
2. The x_group part is rearranged to facilitate processing by group. The term 'group' here refers to the set of convolutional layers with different convolutional kernel scales.
3. Convolutional layers with different kernel sizes (e.g., 3 \(\times\) 3, 5 \(\times\) 5) are applied to each group of x_group to capture features at different scales.
4. The processed x_group is connected to the unprocessed x_cheap in the channel dimension to combine features at different scales with the original features.
5. Finally, a 1 \(\times\) 1 convolutional layer is applied over the fused features in order to integrate all features while keeping the number of channels constant and outputting the final feature tensor.  
As illustrated in Equation (1), this represents the calculation process for the MCC module.  
\[MCC(X)=CBS\big{(}Concat\big{(}CW_{k_{1}*k_{1}}(x_{1}),\ldots.CW_{k_{n}*k_{n}}(x _{n})\big{)}\big{)} \tag{1}\]  
where X = \([\text{x}_{1},\text{x}_{2}\ldots.\text{x}_{n}]\) denotes splitting the input feature X into multiple heads in the channel dimension, k\({}_{i}\in\{3,5,\ldots,\text{k}\}\) denotes a monotonic increase in the kernel size by 2, and C denotes the convolution operation, where CBS is a combination of convolution, batch normalization, and a sequence of activation functions, SILU.  
To assess the efficiency of the MCC module, this study compares the number of parameters in the YOLOv8 model following the implementation of both the C2f and MCC modules. Table 2 shows the parameter changes after the MCC module. The findings indicate that the MCC module reduces the number of parameters by approximately 25% relative to the C2f module, while also achieving close to a 20% reduction at the model level. This significant decrease enhances both the network efficiency and inference speed. By retaining the first two C2f modules within the Backbone and maintaining one C2f module in the Head section, this study strikes a balance between the model performance and parameter efficiency. This approach ensures that sufficient feature information is captured effectively, allowing for high accuracy in object detection despite reductions in the parameters.  
Figure 2: Schematic diagram of the MCC module.