## 2 Method
### Decode Architecture  
In the decoder, we extract low-level features from the intermediate layers of the backbone network ResNet [51]. These low-level features have higher spatial resolution and richer detail information. Then, through the ASPP module, we capture contextual information at different scales, generating features with different dilation rates. Next, the feature maps generated by the ASPP module are upsampled to align with the input image's dimensions. Subsequently, the low-level features and the upsampled high-level features are fused to combine fine-grained feature information with global context information. In the improved decoder, before fusing the encoder features with the shallow features from the backbone network, we first stack and fuse them with the fused features from the encoder's feature fusion branch and then fuse them with the shallow features. The original DeepLab V3+ [52] improves features by passing the final fused feature map with shallow features through a 3 \(\times\) 3 standard convolution. To reduce the model's parameter count, we replace the 3 \(\times\) 3 standard convolution with a 3 \(\times\) 3 depthwise separable convolution [53], as shown in Figure 6. Finally, the fused features are fed into the final classifier to classify each pixel, producing the final semantic segmentation result. Figure 5: Fusion Unit structure. Figure 6: Depthwise Separable Convolution. The ASPP module, as shown in Figure 7, applies one 1 \(\times\) 1 \(\times\) 256 convolution and three 3 \(\times\) 3 \(\times\) 256 convolutions with dilation rates of 6, 12, and 18 to capture multi-scale information in parallel. By combining multi-scale features, it can classify objects of different sizes. The module performs global average pooling on the final feature map to obtain global image-level features, which helps mitigate the issue of weight vanishing when the dilation rate increases. In the decoder, two concat operations are performed to effectively fuse features from different scales and levels. The first concat operation merges the high-level semantic features obtained from the ASPP module with the low-level details from earlier network layers. This fusion allows for the model to combine the context of the image with the fine-grained spatial details, improving segmentation accuracy, especially for small objects and object boundaries. The second concat operation combines multi-scale features extracted from the ASPP module at different dilation rates with the fused low-level features. This process enables the model to handle various object sizes and more complex structures, thereby enhancing its ability to segment objects in a diverse range of contexts. Through the edge extraction branch, we obtained rich edge information and employed the edge feature-weighted Dice Loss [54], a statistical metric used to measure the similarity between two sets. The advantage of Dice Loss lies in its robustness to class imbalance, making it particularly suitable for segmentation tasks where there is a significant difference in the number of positive and negative samples. This ensures that the model places more emphasis on the segmentation of small classes. The Dice coefficient, a similarity measure function used to calculate the similarity between two samples, is defined as follows:  
\[\textit{dice}=\frac{2|X\cap Y|}{|X|+|Y|} \tag{5}\]  
\(|X\cap Y|\) represents the intersection between \(X\) and \(Y\); \(|X|\) and \(|Y|\) represent the number of elements in \(X\) and \(Y\), respectively. The coefficient 2 in the numerator accounts for the double counting of \(X\) and \(Y\) in the denominator. In semantic segmentation tasks, \(X\) represents the ground truth segmentation image, and \(Y\) represents the predicted segmentation image. Figure 7: Atrous Spatial Pyramid Pooling. The Dice coefficient calculation approximates \(|X\cap Y|\) as the dot product between the predicted and ground truth segmentation images and then sums the element-wise results of the dot product. The calculation method is as follows:  
1.