## 3 Image Recognition Method
### Module Improvements  
The DenseBlock module is the main module in the DenseNet model, and its role is to extract features from images. However, there is a problem that the arrangement of various functional layers is not reasonable enough in this module, so there is still a lot of room for optimization. In this regard, this paper refers to DPRN and improves it by deleting some layers and adjusting the convolution kernel. The improved module is shown in Figure 7:  
Figure 6: The flow of the proposed image recognition method.  
Figure 7: The improved Dense Block.  
The literature [41] pointed out that the use of a large number of ReLU layers in each module may have some negative impacts on the overall performance of the module, and removing the first ReLU in each module can improve the performance to a certain extent. Therefore, the first ReLU layer of the original module is first deleted to appropriately enhance the performance. Secondly, according to the literature [13], it is shown that using a \(1\times 1\) convolution kernel to raise and lower the dimension and using a \(3\times 3\) convolution kernel to improve features requires fewer parameters than directly using a \(3\times 3\) convolution kernel for feature extraction while raising and lowering the dimension. More convolutions are superimposed on the receptive field of size, which can extract richer features and have higher computational efficiency. Therefore, this paper splits the \(3\times 3\) convolution block in the original Dense Block into a \(3\times 3\) convolution block with constant dimension and a \(1\times 1\) dimension reduction convolution block. The calculation formula of the convolution parameter quantity is described as follows.  
\[p=K_{1}\times K_{2}\times\mathbf{M}\times\mathbf{N} \tag{1}\]  
In the formula, \(p\) is the amount of convolution parameters. \(K_{1}\), and \(K_{2}\) are the length and width of the convolution kernel and they are both equal generally. \(\mathbf{M}\) and \(\mathbf{N}\) are the input and output feature map dimension vectors of the convolution layer, respectively. Formula (1) generally does not consider the bias parameters and the effect of the Batch Norm, thus simplifying the calculation. It can be seen from Formula (1) that in the case of using a pyramid structure, splitting the \(3\times 3\) convolution can reduce the amounts of parameters.  
In addition, DenseNet mainly processes the input features through the Dense Block module, and there is only one convolution kernel for feature extraction, which is inevitably relatively single, and it is easy to lose the information in the picture. In order to make full use of the features without increasing too many parameters, a new dilated convolution block (DC Block) based on the dilated convolution method is designed. This problem is improved by adding this module in Figure 8.  
In the DC Block, the input data is first normalized and activated by the BN layer and the ReLU layer, and then calculated by a \(3\times 3\) dilation rate [42] with a dilation rate of 2, and then normalized by a BN layer. Output to complete the calculation of the entire DC Block. The equivalent convolution kernel size calculation formula for dilated convolution is:  
\[K=k+(k-1)\times(r-1) \tag{2}\]  
where \(K\) indicates equivalent convolution kernel size, \(k\) indicates dilated convolution kernel size, and \(r\) is dilation rate. According to the calculation of Formula (2), the equivalent convolution kernel size of the \(3\times 3\) dilated convolution in the DC Block is 5, which reduces the parameters and can approximate the effect of the \(5\times 5\) convolution kernel, which not only changes the original DenseNet. It solves the problem of a single convolution kernel in the model and improves the richness of feature extraction in the model. It can read image features from different angles and make full use of different features for analysis.  
Figure 8: DC Block architecture.