# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 2 Materials and Methods
### U\({}^{2}\)-Net Retrofit
#### 2.2.1 U\({}^{2}\)-Net Network and Convolutional Block Attention Module (CBAM)
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Training Set Composition** & **Training Set 1** & **Training Set 2** & **Training Set 3** & **Training Set 4** & **Training Set 5** \\ \hline Original training set (1200) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\
90\({}^{\circ}\) rotation (1200) & & & & \(\surd\) & \(\surd\) \\
180\({}^{\circ}\) rotation (1200) & & & & \(\surd\) & \(\surd\) \\ Horizontal mirroring (1200) & & & & \(\surd\) & \(\surd\) \\ Brightness enhancement (1200) & & & & \(\surd\) & \(\surd\) \\ Brightness reduction (1200) & & & & \(\surd\) & \(\surd\) \\ Random noise (1200) & & & & \(\surd\) & \(\surd\) \\ Gaussian blur (1200) & & & & \(\surd\) & \(\surd\) \\
0.1 (1200) & \(\surd\) & \(\surd\) & & \(\surd\) \\
0.15 noise (1200) & & & \(\surd\) & & \(\surd\) \\
0.20 noise (1200) & & & \(\surd\) & & \(\surd\) \\ Total number of images & 1200 & 1200 & 4800 & 9600 & 13,200 \\ \hline \end{tabular} Note: A checkmark indicates that the dataset used a type of augmentation; \"0.1 noise\" means 1200 images of the training set with 0.1 level of noise added.  
\end{table} 
Table 1: Training set composition. dimensions. The mean and maximum pooling aggregate the spatial information of the feature map, which is then mapped to the weights of each channel through the convolutional layer or fully connected layer. The original features are multiplied with this vector in the channel dimension to obtain the weighted feature map, and the size of the weights reflects the degree of relevance and importance of the features in the layer (channel) for the key information. CBAM contains a spatial attention mechanism and a channel attention mechanism, and the structure is shown in Figure 3. The CBAM is a simple yet effective attention module for feedforward CNNs. The module sequentially infers attention maps along two separate dimensions, channel and spatial, and then multiplies the attention maps with the input feature map for adaptive feature refinement. It can be seamlessly integrated into any CNN architecture with negligible overhead and is end-to-end trainable along with the base CNNs to improve the accuracy of the CNN [25]. [PERSON] et al. proposed an improved YOLOv4-based pavement damage detection model. The model improves the saliency of pavement damage by introducing a convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on detection accuracy. The results indicate that embedding CBAM into the neck and head modules can effectively improve the detection accuracy of the YOLOv4 model [26]. [PERSON] et al. proposed an attention-based feature pyramid module (AFPM), which integrates the attention mechanism based on a multilevel feature pyramid network to efficiently and pertinently extract high-level semantic features and low-level spatial structure features, thus improving the accuracy of instance segmentation [27]. According to the above findings, the convolutional block attention module (CBAM) can independently learn the importance of each channel and space feature, recalibrate the channel and space features, and improve image classification performance [28]. Therefore, in this study, CBAM was added to multiple locations of U\({}^{2}\)-Net. Figure 1: CAM structure. Figure 3: CABM network structure. Figure 2: SAM network structure.