A Lightweight Network with Multi-Scale Information Interaction Attention for Real-Time Semantic Segmentation

Real-time semantic segmentation is an important field in computer vision. It is widely employed in real-world scenarios such as mobile devices and autonomous driving, requiring networks to achieve a trade-off between efficiency, performance, and model size. This paper proposes a lightweight network with multi-scale information interaction attention (MSIANet) to solve this issue. Specifically, we designed a multi-scale information interaction module (MSI) is the main component of the encoder and is used to densely encode contextual semantic features. Moreover, we designed the multi-channel attention fusion module (MAF) in the decoder part, thereby realizing multi-scale information fusion through channel attention mechanism and spatial attention mechanism. We verify our method through numerous experiments and prove that our network possesses fewer parameters and faster inference speed compared to most of the existing real-time semantic segmentation methods in multiple datasets.