## 2 Materials and Methods
### Dataset
#### 2.2.2 Backbone  
The backbone is the foundation of the entire network, extracting basic image features and obtaining as comprehensive information as possible for subsequent detection processes. Different backbones consist of different modules arranged in specific ways and sequences; this area of research is already very advanced. Based on ConvNeXt, we introduced spatial reconstruction units and channel reconstruction units to address potential redundancy issues caused by large convolution kernels in downstream tasks. Simultaneously, inspired by RepLKNet, which also uses large convolution kernels, we incorporated GRN and SE modules to enhance module stability. As illustrated in Figure 4. ConvNeXt is a CNN architecture optimized based on ResNet-50, drawing inspiration from the Swin-Transformer's approach and training methods. It underwent optimization in training hyperparameters, structural proportions, network depth, output dimensions, etc., in conjunction with the final structure honed from ResNeXt. From the top-level feature extraction layer to the middle inverted bottleneck structure stacking, to the final high-dimensional output feature classification layer, overall improvements can be outlined in the following aspects: (i) In each stage of the original ResNet-50, the stack numbers for each block are 3:4:6:3; in ConvNeXt, this has been modified to 3:3:9:3, similar to the block stacking in Swin-Transformer. (ii) In ResNet-50 blocks, the bottleneck first reduces dimensions, then extracts features, and finally increases dimensions. However, as shown in Figure 4, the bottleneck in ConvNeXt is designed to perform feature extraction first, followed by dimension reduction, and finally dimension increase. (iii) The size of the convolutional kernel has been changed from 3 \(\times\) 3 in ResNet to 7 \(\times\) 7. (iv) Its activation function has also been replaced from ReLU to GELU, and the frequency of activation function usage has been reduced. (v) Its normalization has been changed from batch normalization to layer normalization, and the frequency of normalization usage has been reduced. Figure 4: Comparison block of modules from different network architectures. ConvNeXt V2 further improves its block by addressing the cosine distance loss of features after multiple training sessions. It introduces a global response normalization module to enhance the diversity of features after multiple training sessions, improving judgment accuracy and training efficiency. By mapping \(X_{i}\) to the global function vector \(g_{x}\),  
\[G(X):=X\in R^{H\times W\times C}\to g_{x}\in R^{C} \tag{1}\]  
By aggregating features through L2 norm to improve performance  
\[G(X)=g_{x}=\{\|X_{1}\|,\|X_{2}\|,\ldots,\|X_{C}\|\}\in R^{C} \tag{2}\]  
Subsequently, the corresponding normalization function \(N\) uses split normalization  
\[N(\|X_{i}\|):=\|X_{i}\|\in R\rightarrow\frac{\|X_{i}\|}{\sum_{j=1,\ldots,C} \subset\|X_{j}\|}\in R \tag{3}\]  
Obtaining the global response normalization module  
\[X_{i}=X_{i}*N(G(X)_{i})\in R^{H\times W} \tag{4}\]  
For ease of training, two learnable parameters and a skip connection layer are added, resulting in the GRN module  
\[X_{i}=\rho*X_{i}*N(G(X)_{i})+\sigma+X_{i} \tag{5}\]  
where the \(\rho\) and \(\sigma\) are learnable affine transform parameters. This module is added to the original ConvNeXt Block, enhancing overall feature richness and classification accuracy. However, in the reexamination of large convolution kernels, the large receptive field brought by large convolution kernels may not directly translate into feature richness. Due to the increased receptive field of the convolutional kernel, depth-wise separable convolution is generally used to balance computational costs. However, this change may reduce accuracy compared to regular convolution operations. To overcome this issue, SE Attention is introduced to learn relationships between different channels, potentially compensating for the drawbacks of this operation.