## 2 An ESNet of Real-Time Semantic Segmentation Based on DCNN  
### Basic Principles of CNN  
The LeNet-5 network proposed by LeChen can effectively recognize handwritten digits, bring the CNN back into people's view, and lay the basic structure of the CNN. Additionally, CNN is a branch of deep neural network (DNN). The diagram of the network is shown in Figure 1.  
Convolutional layerThe core layer in CNN is the convolutional layer, which contains multiple convolution kernels. Different convolution kernels can extract the local features of the images in the input network. In general, low-level convolutional layers can only extract low-level local features such as corners and edges. In contrast, the extraction of high-level feature representations requires high-level convolutional layers [14; 15; 16]. The number of convolution  
Figure 1: Architecture of LeNet. kernels, the step size of convolution, the size of the convolution kernel, and the padding are the main parameters of the convolutional layer. The size of the convolution kernel is set to K \(\times\) K, the step size of convolution is set to 1, and no padding. The obtained convolution calculation is shown in Equation (1). \[c_{i,\,j}^{s}\ =\ \sum_{m=0}^{K}\sum_{n=0}^{K}\sum_{m=0}^{K}W_{m,n}^{s}X_{m+i,n+ j}^{s}+b^{s} \tag{1}\]  
In Equation (1):  
\(X\) is the single-channel feature map of the input network. \(C\ (i,j)\) is the element of the \(i\)-th row and the \(j\)-th column of the feature map output from the network structure. \(W\) is the weight value. \(b\) is the bias value. \(S\) is the different biases and weights of different convolution kernels. The process of convolution operation is shown in Figure 2.  
(2) Pooling layer  
The pooling layer, also known as the down-sampled layer, reduces the dimension of the feature image by down-sampling. The redundant feature information is reduced, and the dimension reduction of the data is realized. In this process, the quality of the picture will not be affected. The pooling layer can be divided into average pooling and maximum pooling. Average pooling refers to dividing the input feature image into multiple regions and calculating the average value as the output. Max pooling takes the maximum value in each region as the output [17; 18]. The diagram of the two pooling operations is shown in Figure 3.  
(3) Fully connected layer  
The fully connected layer is composed of neurons on a single layer, and its neuron nodes are connected to the neuron nodes of the previous layer. The fully connected layer is usually stacked behind the convolutional layer in the CNN, and the neural network (NN) can contain multiple fully connected layers to integrate the extracted feature information. Suppose the role of the pooling layer and the convolutional layer is to map the original data to the feature space of the hidden layer. In that case, the role of the fully connected layer is to map the learned feature representation to the sample label space. It is equivalent to the \"classifier\" in the entire CNN [19; 20]. (4) Activation function  
The activation function plays a vital role in the composition of the NN. If there is no activation function in the NN, it is no different from a single-layer network and can only perform some simple linear calculations. The activation function can make the network more flexible to adjust the mapping function, and the function is often nonlinear such that the DNN can fit any complex function [21; 22; 23]. Common activation functions are Sigmoid activation function, Tanh activation function, Relu activation function, Leaky Relu activation function. The plot of these activation functions is shown in Figure 4.  
Figure 2: The process of the convolution operation. The most commonly used activation function in the early classification network is the Sigmoid activation function, and its expression is shown in Equation (2). \[\text{f(x)}=\frac{1}{1+\text{e}^{-\chi}} \tag{2}\]  
Figure 4: The plots of the activation functions.