# Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN
## 3 Model Building  
### Dilated CNN  
In the CNN, each convolutional layer consists of a set of convolutional kernels through which the features of the image are extracted [22]. In this process, each element of each output feature map corresponds to a region on the input feature map, as shown in Figure 2. Where \(5\times 5\) matrix is the target image and numbers are the features. \(3\times 3\) matrix is the convolution kernel. The convolution kernel slides on the input data. At each position, the convolution kernel is multiplied by a local area of the input data element by element, and the results of all the products are summed. The result of the summation is taken as the value of the corresponding position in the output feature map. Repeat the above steps until the complete input data are traversed to generate a complete output feature map. The choice of convolution kernel is very important, as it determines the filtering method. Different convolution kernels will obtain different features. The specific formula of the convolution operation is:  
\[C(x,y)=\sum_{i=1}^{m}\sum_{j=1}^{n}\left(X(x+i-1,y+j-1)\cdot K(i,j)\right)+b \tag{7}\]\(C(x,y)\) represents the value of the output feature map at position \((x,y)\), \(X\) represents the input feature map, \(K\) represents the convolution kernel, \(m\) and \(n\) represent the size of the convolution kernel, and \(b\) is the bias. A limit of convolutional layers is that they greatly increase the number of parameters in the output tensors compared to the input ones, especially when many filters are involved, then the magnitude of the tensors grows exponentially. For this reason, a pooling layer usually succeeds a convolutional one. Its purpose is to sub-sample the feature map by retaining only the most attractive information extracted by the convolutional layer. There are many possible pooling functions, but in this work, the MaxPooling one is adopted, which takes only the max value out of a predefined sub-matrix [23]. A fully connected layer is a common layer type in convolutional neural networks. A fully connected layer is generally used as the last few layers of the CNN model, which is used to compress and transform the features extracted from the convolutional layer and the pooling layer, and finally map the features to the output category. In the fully connected layer, each neuron is connected to all neurons in the previous layer, and each connection has a corresponding weight parameter to adjust the weight of the input signal. The receptive field is the size of the region on the input image corresponding to each output feature map element in a convolutional neural network. It represents the perceptual range of each location in the network, i.e., the size of the input image region used to compute the output features. The size of the sensory field depends on several factors:  
1. Convolution kernel size. Receptive field controlled by convolutional kernel size. Larger convolution kernels can capture more contextual information, but they also increase the amount of computation and the number of parameters. 2. The number of convolutional layers. The perceptual field of each output feature map increases as the depth of the network increases because the output feature map of each convolutional layer is a summary of a certain region on the input feature map of the previous layer. 3. Step size and pooling operation. The step size is the distance the convolution kernel slides over the input feature map, and the pooling operation downsamples the input feature map. Larger step sizes and pooling operations reduce the size of the sensory field because each output feature map element is associated with only a portion of the input feature map [24; 25]. The size of the receptive field is important for feature extraction. A smaller receptive field may not be able to capture large-scale features in an image, while a larger receptive field can provide more global contextual information. Therefore, this chapter adds dilated convolution.