Abstract: High-precision semantic segmentation enables autonomous vehicles to obtain detailed environmental perception. To address the limitations of traditional methods on fisheye images, such as poor edge segmentation, low accuracy, and insufficient training data, we propose RSCAMamba, a model specifically designed for fisheye image segmentation. A zoom augmentation method is employed to transform standard datasets into fisheye datasets, allowing effective modeling of fisheye distortions and ensuring robustness across diverse scenarios. RSCAMamba first adopts a Swin Transformer encoder to capture global feature representations. Second, we propose the restricted spatial-channel attention module. By integrating one-dimensional and two-dimensional restricted deformable convolutions, the module adaptively models distortion-aware nonlinear features and effectively captures anisotropic deformations. Consequently, it provides more accurate representations of strip-like structures and irregular edges. In addition, a channel reduced and edge increased module further enhances edge details, alleviating distortion-induced degradation. Finally, the Mamba module fuses global features, captures long-range dependencies, and reduces redundancy across scales. This helps the model detect complete objects and preserve spatial continuity. Experimental results indicate that, compared with Mask2Former, RSCAMamba achieves a 1.88% improvement in mIoU on the WoodScape public dataset and a 3.30% improvement on the CityScapesFisheye synthetic dataset, demonstrating superior segmentation performance.

摘要： 高精度的语义分割技术能为自动驾驶车辆提供详尽的环境感知信息。针对传统语义分割方法在鱼眼图像中存在的边缘分割效果差、整体精度低以及训练数据缺乏的问题，提出了一种专用于鱼眼图像语义分割的模型RSCAMamba，并基于变焦增强方法，将普通图像数据集转换为鱼眼图像数据集，旨在有效捕捉鱼眼图像的畸变特征、提升模型的准确性，同时在不同场景下验证模型的鲁棒性。方法首先采用Swin Transformer作为编码器，以准确地建模输入数据的全局特征表示；其次，提出了受限空间通道注意力模块，通过引入一维和二维的受限可变形卷积，在自适应地捕获各向异性的畸变的同时，实现了基于畸变信息的非线性特征建模，从而更准确地刻画条状物体与不规则边缘；此外，采用通道收缩与边缘扩展模块进一步增强图像的边缘细节，缓解因畸变导致的边缘分割性能退化；最后，采用Mamba模块以实现全局特征融合，在捕捉长程依赖关系的同时减少多尺度特征中的冗余信息，使模型能够准确识别完整物体并保持区域空间的连续性。实验结果显示，与Mask2Former相比，RSCAMamba的关键性能指标mIoU在WoodScape公开数据集上提升了1.88%，在CityScapesFisheye合成数据集上提升了3.30%，具有较优的分割性能。