Multi-scale hybrid mamba network for lightweight semantic segmentation

Real-time semantic segmentation is a core component of environment perception in autonomous driving systems. Models based on convolutional neural networks (CNNs) and Transformers have achieved significant progress in this task. However, both paradigms face difficulty in efficiently capturing multi-scale features under strict lightweight computational constraints. To address these limitations, this paper proposes a Multi-scale Hybrid Mamba Network (MHMNet) by enhancing a state space model with linear computational complexity and integrating it with the Multi-branch Feature Aggregation Block (MFAB). The proposed hybrid architecture adopts an asymmetric encoder-decoder design. In the encoder, MFAB enables cross-scale and multi-dimensional feature extraction, while the decoder employs a Hybrid Mamba module to collaboratively model local details and global contextual information. Subsequently, the Gated Feature Selection Head (GFSH) is introduced to suppress noisy channel interference and enhance the contribution of discriminative features, producing the final segmentation output. This design significantly reduces computational complexity while preserving segmentation accuracy. The proposed MHMNet contains only 0.77M parameters and achieves mean Intersection-over-Union (mIoU) scores of 71.48% and 69.5% on the Cityscapes and CamVid benchmarks, respectively. Experimental results demonstrate that the proposed model maintains competitive segmentation accuracy under a substantially reduced parameter budget, effectively alleviating the trade-off between accuracy and efficiency in lightweight segmentation models and achieving a favorable balance between performance and computational complexity.