## 1 Introduction
As demonstrated in Figure 2, this approach overcomes existing methods' deficiencies in small target segmentation (Figure 2a) through an edge-assisted detection module that significantly improves boundary delineation (Figure 2b) while maintaining computational efficiency. The framework further incorporates innovations from multi-level attention networks [39] and thermal feature utilization [40], achieving robust performance across diverse observational modes through adaptive multi-scale fusion and edge-aware feature refinement. Secondly, in acquiring brightness temperature data of ship compartments through infrared imaging, effectively detecting abnormal thermal variations emerges as a critical challenge for maritime safety monitoring, necessitating comprehensive evaluation of detection methodologies. Current approaches for abnormal temperature identification primarily encompass single-parameter methods, multi-parameter fusion techniques, and visual detection strategies. Single-parameter solutions such as thresholding [41; 42], trend analysis, and slope analysis prioritize computational efficiency but exhibit limited accuracy. While threshold-based detection identifies anomalies through predefined thermal boundaries and trend analysis monitors temporal temperature patterns, these simplistic methods generally underperform compared to advanced alternatives. Recent machine learning advancements have introduced sophisticated prediction models, including [PERSON] et al. 's Multi-LSTM Convolutional Neural Network (M-LCNN) for sea surface temperature forecasting [43], [PERSON] et al. 's XGBoost-based ceramic thermal analysis [44], and [PERSON] et al. 's XGBoost application in wildfire brightness threshold estimation [45]. Building upon these developments, our work employs XGBoost [46] to model ship cabin temperature transfer processes using infrared-derived thermal data, capitalizing on its generalization capacity, computational efficiency, and overfitting resistance for abnormal temperature prediction. Conversely, multi-parameter fusion methods enhance detection accuracy through comprehensive feature integration, as evidenced by [PERSON] et al. 's extreme gradient boosting for sea temperature analysis [47], [PERSON] et al. 's CNN-based ensemble learning for fire detection [48], and [PERSON] et al. 's fuzzy neural network combining temperature, smoke, and CO concentrations [49]. Addressing challenges specific to ship compartment monitoring, particularly signal loss and false alarms arising from parameter complexity, our proposed framework extends these multi-feature fusion principles by developing a temperature-centric detection architecture optimized for maritime thermal anomaly identification in infrared imagery. Figure 2: Comparison with current advanced semantic segmentation algorithms. To address the above challenges, the contributions of this paper include the following: we propose a novel ship segmentation framework designed specifically for infrared ship scenes that incorporates contour enhancement and multi-level feature fusion. This framework accurately predicts ship masks in infrared images, significantly improving segmentation precision, especially for small target features and ship edges. To address the scarcity of infrared data for ships on the water, we captured over 2000 images of ships in the Pearl River region, annotating 1000 of them with more than 1000 target masks. To tackle the challenges of edge blurring and low background contrast in infrared images, we developed an edge enhancement network that strengthens edge features, thus improving segmentation accuracy. Additionally, for segmenting small maritime targets, we employed a multi-level feature fusion structure that retains shallow feature information, effectively achieving segmentation of small ships. Finally, we constructed a transverse temperature model for ships, extracting temperature information from infrared images to enable the prediction of ship temperatures.