Lightweight attention-guided redundancy-reuse network for real-time semantic segmentation

Semantic segmentation is a critical topic in computer vision, and it has numerous practical applications, including mobile devices, autonomous driving, and many other fields. However, in these application scenarios, it is often essential for the segmentation models to achieve a balance between efficiency and performance. A lightweight attention-guided redundancy-reuse network (LARNet) was proposed to address this challenge in this paper. Specifically, the multi-scale asymmetric redundancy reuse (MAR) module was designed as the main component of the encoder for dense encoding of contextual semantic features. Furthermore, the efficient attention fusion (EAF) module was established for multi-scale information fusion via the channel and spatial attention mechanisms in the decoder. A series of experiments were conducted to verify the proposed network. The results of tests on multiple datasets suggest that the network has higher accuracy and faster speed than the existing real-time semantic segmentation methods.