LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation

Real-time semantic segmentation has extensive applications in practical fields such as autonomous driving. Despite the significant progress made by deep learning in semantic segmentation, the performance of real-time methods remains suboptimal. Many existing high-accuracy networks achieve precision by utilizing complex architectures with massive parameters. Subsequently, lightweight networks have generally improved inference speed at the expense of segmentation accuracy. Balancing accuracy and speed is crucial in real-time semantic segmentation. This article proposes a novel lightweight semantic segmentation model, a lightweight and accurate balanced network (LANet), designed for real-time applications. LANet combines standard convolution and depthwise convolution, designs a multiskip concat bottleneck (MSC-B), and introduces feature enhancement based on pyramidal dual attention (FEPDA) modules and a feature fusion module based on multiscale state space models (FFMSSM). These components are designed to synergistically balance model accuracy and inference speed. Experimental results demonstrate that LANet significantly improves inference speed and reduces model parameters while maintaining high accuracy, making it ideal for devices with limited resources. Without pretraining, LANet achieves a mean intersection over union (mIoU) of 76.9% at 130.0 frames per second with 1.33M parameters on an RTX 3090 GPU.