## 1 Introduction  
The rapid development of computer vision and autonomous driving technology has driven the advancement of Unmanned Surface Vehicles (USVs) and has also led to an increasing demand for image analysis in complex environments. Similar to autonomous driving, semantic segmentation in different waterway scenes is a key component of USVs' environmental perception tasks. However, in some scenes, the uncertainty in the size and quantity of obstacles, as well as the impact of weather and reflection conditions on camera imaging, can blur the edges of obstacles in the line of sight. This makes it difficult for traditional semantic segmentation algorithms to achieve good results in such complex waterway scenes. The accuracy of the segmentation algorithm determines whether the USV can detect and avoid nearby obstacles in a timely manner to achieve a high level of autonomous navigation, which is crucial for ensuring the safe navigation of vessels and improving navigation efficiency. Thanks to the exploration in the field of autonomous driving, some works [1; 2; 3] have introduced the integration of various sensors such as RADAR and LIDAR and achieved certain results. However, considering the aspect of lightweight, cameras [4; 5; 6] still show great potential as portable and flexible sensors. In recent years, semantic segmentation networks have demonstrated the ability to obtain rich feature information in complex scenes and achieved significant segmentation results [7; 8; 9]. At the same time, attention mechanisms have been proven to significantly enhance the model's global capturing ability regarding key information [10]. However, the encoder-decoder structure based on the Transformer architecture [11] has difficulties in real-time or resource-constrained scenarios like USVs due to hardware utilization defects during attention operations and quadratic time complexity issues. Mamba [12] addresses the two major issues of the Transformer architecture by using hardware-parallel optimization and selective state space to efficiently capture feature information of long sequences, achieving a powerful global context modeling ability with linear complexity. Benefiting from the successful practice of Transformers in the field of vision, Mamba has been rapidly applied to image tasks, especially in the field of image segmentation, such as VMamba [13], VM-UNet [14], VM-UNetV2 [15], CM-UNet [16], etc. They have proven that high-precision image segmentation can be achieved by stacking improved Mamba blocks combined with UNet [17; 18]. However, Mamba uses forget gates [19] to provide positional information and local bias, forcing the model to adopt a recursive approach for training and inference. This recursive computation inevitably reduces the model's throughput. Therefore, using a deep dual-branch backbone for feature extraction combined with Mamba-2 [11] for global context information extraction has become a simple and practical strategy. To enhance the scene-parsing capabilities of USVs operating in complex waterway environments under various weather conditions and reduce the misidentification rate of edges, this paper proposes a Lightweight Dual-branch Mamba Network based on image processing. Unlike the blocky U-Net architecture [14; 15; 16; 20; 21] and encoder-decoder frameworks [22; 23; 24; 25], we are the first to attempt the use of a deep dual-branch backbone combined with Mamba-2 [11] to efficiently obtain the final feature map. Additionally, we have identified that directly fusing feature maps with high-resolution discrepancies can lead to imbalances and mismatches in features. To address this, we designed a Convolution-based Recombine Attention Module to apply weighted attention to both spatial and channel dimensions of the features, refining edge information. In consideration of the spatial information loss and directional sensitivity when images are unfolded into one-dimensional sequences, a Hilbert curve scanning mechanism is proposed to achieve multi-scale feature serialization.