PBSNet: pseudo bilateral segmentation network for real-time semantic segmentation

Abstract

Achieving real-time performance while maintaining high accuracy in semantic segmentation can be a challenging task. Many existing methods adopt multi-branch architectures to extract both spatial and semantic information, resulting in increased computational complexity and a lack of communication between branches. We propose a pseudo bilateral segmentation network (PBSNet) that can extract rich spatial and semantic features from a single path, without incurring additional computational cost or time consumption. Our proposed PBSNet utilizes a semantic enhancement module to explore the relationship between high-level semantic features, an interchange module to enhance feature representation through bi-directional vertical propagation and adaptive spatial attention, and an attention fusion module to aggregate multi-scale features to produce the final segmentation prediction. Our results on the Cityscapes dataset demonstrate the superiority of PBSNet over state-of-the-art methods, achieving a balance of accuracy and efficiency with 74.52% mean intersection over union and 82.5 frames per second.