## 2 Method  
### IERNet Architecture  
We propose an enhanced version of IERNet, a novel data fusion network consisting of two encoders and one decoder. The encoders are responsible for extracting features from input images, while the decoder restores the resolution. The two encoders extract thermal imaging data from two different observation modes. Figure 3 illustrates the overall architecture of our IERNet. We use ResNet as the backbone of the encoders and incorporate an edge feature enhancement branch into the network. This branch emphasizes thermal imaging data by using pseudo-color images to highlight edge information. The pseudo-color effect is achieved by setting a temperature threshold to clearly identify high-temperature areas, which are then highlighted in red to distinguish them from the surrounding environment. Additionally, noise reduction is applied to the original data using bilateral filtering, and non-uniformity correction is performed to compensate for the response differences between pixels in the infrared detector. These operations significantly improve image quality and the accuracy of temperature information. The temperature values of pixels are mapped to grayscale values, and regions with temperatures exceeding the threshold are highlighted in red, ensuring more accurate processing of high-temperature areas. This method extracts richer edge features from pseudo-color data, which contain stronger edge information, and enhances spatial perception through the CBAM. These features are then merged with grayscale image feature maps. Furthermore, in the deep feature layers, edge features are extracted using the Sobel operator and concatenated with low-level feature maps from the encoder, further enhancing feature fusion. The Fusion Unit strategically combines these diverse feature sets using techniques like Depthwise Separable Convolution, improving computational efficiency and reducing model complexity. Finally, the fused feature output undergoes deeper semantic information extraction in the Atrous Spatial Pyramid Pooling(ASPP) module, which is then fused with shallow information. These operations enable the final output features to preserve spatial details while incorporating higher semantic information. The fused features are then passed to the decoder module, enhancing the segmentation performance of the image.  
In comparison to the DeepLabv3+ algorithm, our segmentation method excels in both edge data acquisition and multi-scale data integration. By emphasizing edge features through the edge feature enhancement branch and utilizing the Sobel operator, our method captures finer edge details, significantly improving segmentation accuracy along object boundaries. This combination of edge enhancement and multi-level fusion is particularly relevant, as it allows for more precise edge detection while also leveraging multi-scale information for a comprehensive understanding of the scene. The integration of these two strategies provides a more holistic approach to segmentation, where edge details are preserved while maintaining the ability to handle objects at different scales. Additionally, our approach effectively utilizes the ASPP module to collect multi-scale information, enhancing performance in handling objects of various sizes and complex structures. These improvements not only make our method more precise but also highlight its novelty in addressing edge sensitivity and multi-scale data integration more effectively than existing algorithms like DeepLabv3+. The details of the encoder-decoder structure, CBAM, Fusion Unit, Depthwise Separable Convolution, Atrous Spatial Pyramid Pooling, and the loss function will be explained in the following sections. The specific workflow is as follows: