## 4 Conclusions  
In this paper, we propose a multi-feature fusion encode-decode semantic segmentation network for infrared ship images. Considering the issues of blurred edges and few target features in infrared images, we introduce an edge feature extraction network to enhance the edge information of the images. At the same time, we design a multi-scale feature fusion module to retain more shallow features. To extract sufficient features, we use ResNet50 as the base network and incorporate the ASPP module to detect ships at multiple scales. Since ship targets are usually small and occupy a small proportion of the image, with background pixels being the majority, we use the data from the edge extraction network as weighting parameters. By adding edge weights into the Dice Loss function, we achieve segmentation of small ship targets in infrared images. In the decoder, we replace standard  
Figure 16: Numerical curve for predicting ship temperature. (**a**) Sampling diagram of ship brightness temperature. (**b**) Horizontal temperature curve. convolutions with dilated convolutions, which reduces the number of parameters while ensuring detection accuracy. Due to the scarcity of infrared ship segmentation datasets, we collected and annotated a large number of infrared images of ships in waterways at night. Experimental results demonstrate that our method significantly outperforms DeepLabv3+ in edge segmentation, achieving an mIoU of 89.17%. When compared to state-of-the-art algorithms like DeepLabv3+, PSPNet, and DANet, our method shows improvements of 6.38%, 13.21%, and 9.16% in segmentation performance, respectively. The prediction of cabin temperature is based on brightness temperature information, assuming the ship as a single heat source. We establish a transverse temperature regression model for ships based on extreme gradient boosting trees. By sampling the transverse temperature from the collected infrared images of ships, we obtain the transverse temperature distribution gradient. Using the engine room, water body, ship boundary brightness temperature, and temperature transfer distance as elements, we predict the overall temperature of the ship. Experimental results indicate that the XGBoost model demonstrates a high degree of fit, with goodness of fit exceeding 70% and an RMSE value of 3.472, reflecting minimal errors in both the training and testing sets. Statistical analysis also shows that less than 0.020% of the predicted ship temperature differences exceed 2 \({}^{\circ}\)C, confirming the model's reliability and accuracy in predicting cabin temperatures. Looking ahead, several potential directions can be explored to enhance this system. One promising avenue is to incorporate attention mechanisms, particularly the spatial attention network, to focus on small or distant ships more effectively, especially in scenarios with complex backgrounds or low visibility. This could allow for better differentiation between ships and surrounding objects by prioritizing the most relevant regions of the image. Another improvement would be the integration of temporal data through a recurrent neural network (RNN) or long short-term memory (LSTM) network, which could capture dynamic changes over time and improve the robustness of segmentation in real-world maritime scenarios, where ships may move across frames or appear intermittently. Moreover, while the model primarily focuses on infrared images, a valuable next step would be to implement multi-modal fusion techniques, combining infrared, visible light, and radar imagery to create a robust detection system that operates effectively in various lighting and weather conditions. By fusing these different data types, the system could enhance its resilience to environmental challenges such as fog, rain, or nighttime conditions, where infrared images alone might be insufficient.