# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 5 Conclusions  
Current semantic segmentation methods often struggle with processing waterbody images under the background noise of changing surface illumination and shoreline reflections, which leads to inaccurate segmentation of water boundaries or small objects like aquatic vegetation. This paper proposes the Fuse-ResNet model to address these challenges for accurate segmentation of water boundaries. The Fuse-ResNet model utilizes ResNet-50 as the backbone for feature extraction and incorporates attention enhancement modules. These modules analyze the extracted features in both channel and spatial dimensions, enabling the model to quickly focus on the target areas in the feature maps, thereby reducing the impact of background noise. A multi-scale feature fusion module is designed based on the FPN, and the ASPP is employed to expand the model's receptive field. The integration of these components enhances the model's ability to fuse diverse semantic information. Experimental results on the USVInland dataset demonstrate that the proposed Fuse-ResNet achieves mIoU and mPA values of 98.45% and 99.25%, respectively, surpassing the performance of mainstream segmentation models. The Fuse-ResNet model's superior inference speed makes it well suited for implementation in the intelligent control systems of USVs. However, the proposed model has some limitations. First, it may struggle with extreme weather conditions, such as heavy rain or fog, where image quality deteriorates and results in lower segmentation accuracy. Second, while the model performs well with water boundary segmentation, it may still misclassify small objects, such as aquatic vegetation or floating debris, in complex water scenes. Third, the model's computational requirements are relatively high, which may affect real-time processing when deployed on resource-constrained devices. Future research can focus on addressing these limitations by enhancing the model's robustness under varying environmental conditions and improving its ability to handle fine details and small objects in the water scene. Additionally, exploring lightweight model architectures for real-time applications in resource-constrained environments would be valuable. Another promising direction could be the integration of temporal information to improve segmentation in dynamic water scenes, where the surface and boundaries change over time.  
Software, Y.Y.; validation, Y.Y. and X.H.; writing--original draft, Y.Y. and N.W.; formal analysis: J.Z. and Y.Y.; conceptualization, N.W. and J.D.; supervision of investigation, J.D. and N.W.; resources, J.D.; writing--review and editing, Y.Y., X.H. and N.W.; supervision, J.D. and N.W.; methodology, Y.Y. and N.W.; project administration: N.W. and X.H.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported partially by the National Natural Science Foundation of China (Grant No. 52161042); by the Guangxi Science and Technology Major Program (2024 AA29055); and by the 100 Scholar Plan of the Guangxi Zhuang Autonomous Region of China (2018).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.