# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 1 Introduction
For water images with complex backgrounds and noise, equal weighting of feature maps makes it difficult for models to prioritize critical regions. To address this, [PERSON] et al. proposed the Convolutional Block Attention Module (CBAM), combining spatial and channel attention modules to adaptively adjust feature weights, improving focus on key areas [26]. Similarly, [PERSON] et al. introduced a boundary attention module (BA-module) with an Adaptive Weights of Multitask Learning (AWML) model to capture useful boundary information [27]. CNN pooling operations and large convolution strides significantly reduce feature resolution, impacting small-scale feature extraction [28]. [PERSON] et al. proposed the Atrous Spatial Pyramid Pooling (ASPP) mechanism, which uses atrous convolutions with various dilation rates to increase the receptive field and capture multi-scale contextual information [29; 30; 31; 32]. [PERSON] et al. combined attention mechanisms with residual structures in ASPP modules to improve multi-scale feature extraction [33]. [PERSON] et al. introduced Feature Pyramid Networks (FPNs) for top-down fusion of high-level semantics and low-level details to extract multi-scale features [34]. However, feature fusion may still result in loss or weakening of some details, making it difficult to capture fine or complex boundaries accurately [35]. The complex and ever-changing nature of aquatic environments has been the most challenging issue for waterbody imaging and recognition, including variations in surface lighting, and reflections from shoreline buildings and vegetation, as well as other obstacles. Traditional waterbody image segmentation methods are generally well suited for scenarios with limited data and computational resources. However, when faced with complex scenes, current deep learning methods often fail to adequately segment waterbodies from images in the absence of sufficient data and computational power. In order to address these challenges, this paper proposes a multi-scale feature fusion model, Fuse-ResNet, which leverages ResNet-50 [36] to extract multi-scale feature maps and subsequently applies the CBAM and ASPP modules to selectively and efficiently extract multi-scale features. The proposed network also incorporates an FPN-based architecture to effectively fuse the extracted features from different scales, thereby significantly improving image segmentation accuracy. The main contributions of this paper are as follows:  
(1) Fuse-ResNet Architecture: The Fuse-ResNet model adopts an encoder-decoder architecture, utilizing ResNet-50 [21] as the backbone network, whose residual structure effectively mitigates the gradient vanishing issues caused by deeper network layers. This design enables the model to fully extract multi-scale semantic features from waterbody images. Furthermore, the attention-enhanced encoder allows the model to focus more effectively on target features, improving its ability to emphasize critical regions and resist background noise. (2) Enhanced Edge Feature Extraction: The ASPP module is embedded in the decoder portion of Fuse-ResNet to capture fine details in images, such as waterbody edges. By leveraging dilated convolutions, the ASPP module expands the receptive field, thereby strengthening the contextual association of multi-scale features. (3) Multi-Scale Feature Fusion: A multi-scale feature fusion decoder, designed based on an FPN, is introduced to effectively integrate features from different levels, and the semantic representation of the fused features can be further enhanced, so as to improve segmentation accuracy. The remainder of this paper is structured as follows: Section 2 details the overall design of the Fuse-ResNet network and provides a comprehensive overview of the submodules. Section 3 describes the datasets, experimental parameters, and evaluation metrics used in this study. Section 4 presents a series of experiments, along with an in-depth discussion and analysis of the results. Finally, Section 5 concludes the paper.