## 1 Introduction  
The all-weather capability of infrared imaging offers extensive applications in ship management, including nighttime anomaly detection, illegal fishing surveillance, and smuggling monitoring. By capturing thermal radiation, infrared imaging enables the acquisition of clear images without the need for visible light, and can penetrate atmospheric obstacles such as smoke and haze. These advantages make it a superior solution compared to traditional visual monitoring technologies like SAR, ORS, and RGB cameras [1]. However, two major challenges remain unresolved in existing studies: the precise segmentation of ship targets from complex maritime infrared backgrounds with low contrast, as illustrated in Figure 1, and the interference of thermal noise. Additionally, accurately predicting temperature anomalies through the quantitative analysis of segmented thermal signatures remains a critical issue. Infrared image segmentation faces significant challenges due to inherent limitations such as low contrast, small target sizes, and blurred boundaries, particularly when distinguishing ships from complex backgrounds in nighttime thermal imagery with elevated noise levels [2; 3; 4; 5; 6; 7; 8; 9]. Research in this domain has evolved from traditional methods to deep learning-based approaches. Conventional techniques including threshold-based [10; 11; 12; 13; 14], clustering [15; 16; 17], and active contour segmentation [18; 19] exhibit critical shortcomings: threshold methods fail to address spatial information despite enhancements like 2D maximum entropy [13], clustering approaches like mean shift [15] and FCM [16] demonstrate noise sensitivity, while active contours struggle with dynamic backgrounds. Recent advances in convolutional neural networks (CNNs) have improved feature extraction through architectures like PSANet's adaptive attention masks [20], DeepLab-based multi-scale attention models [21], and IRDCLNet's fog interference mitigation [22]. Further advancements include [PERSON] et al. [23], who enhanced segmentation accuracy for turbine blades by integrating layered depth-separable convolution blocks into U-Net, and [PERSON] et al. [24], whose multi-level correction network (MCNet) combines attention and edge enhancement modules to better utilize shallow and edge features. Specialized solutions for ship segmentation include [PERSON] et al. [25], who proposed Global Mask R-CNN with Precise RoI Pooling and Global Mask Head to preserve instance-level global information, and [PERSON] et al. [26], who developed a fuzzy inference system integrating thresholding, region growing, and morphology for complete ship extraction. [PERSON] et al. [27] introduced a thermal imaging method combining grayscale morphology reconstruction, multi-feature saliency detection, and novel contour descriptors for small ship detection, while [PERSON] et al. [28] demonstrated significant improvements in nighttime ocean observation through their CGSegNet model. Persistent issues in edge quality and small target segmentation necessitate specialized solutions, particularly given the inherently weak edge features in infrared imagery, where strengthening edge information improves segmentation accuracy [29; 30; 31]. Edge detection research combines traditional operators ([PERSON] [32], [PERSON] [33], [PERSON] [34], [PERSON] [35]) with deep learning innovations like DeepLabv1 [36], Gated-SCNN [37], and DeepLabv3 [38], which enhance boundary precision through dilated convolutions and shape learning. Building upon these foundations, our enhanced IERNet framework addresses critical limitations through multi-modal temperature adaptation and hierarchical  
Figure 1: Example of infrared image segmentation. edge enhancement. By synergistically integrating the Sobel operator's gradient detection with deep feature perception, we establish a dual-strategy architecture optimized for infrared characteristics.