# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 1 Introduction  
Unmanned Surface Vehicles (USVs) are commonly used in applications such as hydrographic surveying and mapping, water quality monitoring, port patrolling, and waterborne search and rescue operations [1, 2, 3, 4]. Their precise control largely depends on accurately segmenting the navigable water boundaries via image sensors. For example, visual sensing systems can be installed to identify safe, navigable regions [5]. However, images captured in aquatic environments are often influenced by variations in lighting and the reflections of buildings, vegetation, and other structures on the water surface. These similar features make accurate water boundary segmentation a challenging task. Therefore, rapid and high-precision segmentation of water-land boundaries is of great significance for the real-time autonomous operation of USVs [6, 7]. Currently, multiple methods have been developed for image-based water segmentation, such as clustering segmentation, thresholding segmentation, and maximum entropy segmentation [8]. Clustering segmentation divides images into clusters of similar features, grouping pixels with similar attributes into different regions. For instance, a Density-Based Spatial Clustering (DBSCAN) algorithm has been proposed to segment images into homogeneousregions and extract water regions using wavelet texture analysis, achieving effective water segmentation [9]. However, this method struggles with distinguishing between bright and shadowed water regions in navigation imaging. Thresholding segmentation separates pixels based on intensity thresholds. A semi-automatic method using local image blocks first applies Gaussian and median filters for preprocessing, divides the image into rectangular regions, and adaptively thresholds each region to achieve segmentation [10]. While computationally lightweight, thresholding is highly sensitive to noise and struggles with segmentation when background and target intensities are similar, leading to poor performance [11]. Maximum entropy segmentation uses entropy to analyze pixel distribution, constructing an entropy function to find optimal thresholds that maximize total image entropy [12]. A new active contour model based on modified symmetric cross-entropy has been proposed, improving segmentation precision by using the median intensity values of object and background regions as region-fitting centers [13]. However, the model's large parameter space increases complexity and makes threshold determination unstable [14; 15]. Recently, deep learning-based semantic segmentation methods have been widely applied to image segmentation thanks to advancements in deep learning [16; 17]. For example, [PERSON] et al. applied Convolutional Neural Networks (CNNs) for water detection in remote sensing images [18]. However, shallow CNN architectures limit detection performance. This issue can be addressed by replacing fully connected layers with convolutional layers for pixel-level segmentation [19]. [PERSON] et al. introduced DeepWaterMap, a model built on the FCN framework, optimizing concatenation methods and reducing parameter count to better suit water segmentation [20]. Still, FCN networks face challenges in handling small water regions or edges, due to feature loss caused by down-sampling. To retain image details, U-Net [21] and SegNet [22] networks adopt a \"U\"-shaped encoder-decoder architecture with skip connections that combine low-level semantic features from encoders with high-level semantic features from decoders. This design mitigates detail loss. [PERSON] et al. combined U-Net with a superpixel-based Conditional Random Field (CRF) model for better small water region segmentation [23]. [PERSON] et al. proposed DAU-Net, a densely connected network with skip connections to enhance edge segmentation by reducing semantic differences through multi-scale feature fusion [24]. However, directly connecting low- and high-level feature maps can confuse feature representation, reducing accuracy [25].