# Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN
## 3 Model Building
### Dilated CNN
It expands the receptive field by introducing dilated means in the convolution kernel, thus increasing the perceptual range of the network and improving the model's ability to perceive large-scale features. In the traditional convolution operation, the convolution kernel slides over the input feature map in fixed steps and performs the convolution operation at each position. Dilated convolution, on the other hand, is performed by introducing a fixed interval between the elements of the convolution kernel, which can be an  
Figure 2: Convolution scheme. arbitrary positive integer, usually denoted by the dilated rate. This increases the range of features that can be acquired. The scheme is presented in Figure 3 below, and the formula is shown in 8 below. \[Y_{ij}=\sum_{m}\sum_{n}X_{i\cdot r+m\cdot d,j\cdot r+n\cdot d}\cdot W_{mn} \tag{8}\]  
where \(Y_{ij}\) denotes the value at position \((i,j)\) in the output feature map, \(X_{i\cdot r+m\cdot d,j\cdot r+n\cdot d}\) denotes the value at position \((i\cdot r+m\cdot d,j\cdot r+n\cdot d)\) in the input feature map, r is the step size of the convolution, \(W_{mn}\) denotes the weight value at position \((m,n)\) in the convolution kernel, and d denotes the dilation rate. Compared with traditional methods, dilated convolution has several advantages. (1) Increasing the receptive field: Increase the receptive field to obtain more feature information. This is useful for dealing with large-scale features or scene understanding tasks. (2) Reducing the number of parameters: While keeping the range of the sensory field constant, dilation convolution can use a smaller convolution kernel size, which reduces the parameter. This helps reduce the computational burden on the model and improves operational efficiency. (3) Maintaining feature map resolution: Ordinary convolution operations will continuously reduce the resolution of the feature map as the number of layers increases. Dilated convolution can maintain the resolution of the feature map by increasing the dilated rate, thus avoiding the loss of information. Since the data processed in this paper are one-dimensional sequence data, the two-dimensional dilated convolution is improved to one-dimensional dilated convolution. The schematic diagram of one-dimensional inflationary convolution is shown in Figure 4. Similar to two-dimensional inflationary convolution, the one-dimensional inflationary convolution expands the sensory field by introducing the dilated rate, thus increasing the network's ability to perceive the contextual information of the sequence data. The specific formula is:  
\[Y_{i}=\sum_{m}X_{i\cdot r+m\cdot d}\cdot W_{m} \tag{9}\]  
Figure 4: Schematic diagram of one-dimensional dilated convolution. Figure 3: Dilated convolution scheme. Where \(Y_{i}\) denotes the value of the i element of the output sequence Y, and \(X_{i:r+i:m\cdot d}\) denotes the value of the element in the input sequence X, where r is the step size of the convolution, d is the dilation rate, and \(W_{m}\) denotes the value of the weight at m in the dilated convolution kernel. One-dimensional inflated convolution has several advantages when dealing with one-dimensional sequential data [26]:  
1. Expanding the sensory field. One-dimensional inflated convolution expands the sensory field by adjusting the dilated rate. A larger dilated rate increases the range of observation of the input sequence by the convolution kernel, thus providing broader contextual information. This is useful for capturing long-term dependencies and understanding associations at long distances in the sequence. 2. Reduced number of parameters. Compared to traditional one-dimensional convolution, one-dimensional inflated convolution can use a smaller convolution kernel size to achieve the same range of sensory fields. By introducing the dilated rate, the number of parameters in the model can be effectively reduced, reducing the computational burden of the model and improving its efficiency. 3. Maintaining sequence length.