Joint pyramid attention network for real-time semantic segmentation of urban scenes

Semantic segmentation is an advanced research topic in computer vision and can be regarded as a fundamental technique for image understanding and analysis. However, most of the current semantic segmentation networks only focus on segmentation accuracy while ignoring the requirements for high processing speed and low computational complexity in mobile terminal fields such as autonomous driving systems, drone applications, and fingerprint recognition systems. Aiming at the problems that the current semantic segmentation task are facing, it is difficult to meet the actual industrial needs due to its high computational cost. We propose a joint pyramid attention network (JPANet) for real-time semantic segmentation. First, we propose a joint feature pyramid (JFP) module, which can combine multiple network stages with learning multi-scale feature representations with strong semantic information, hence improving pixel classification performance. Second, we built a spatial detail extraction (SDE) module to capture the shallow network multi-level local features and make up for the geometric information lost in the down-sampling stage. Finally, we design a bilateral feature fusion (BFF) module, which properly integrates spatial information and semantic information through a hybrid attention mechanism in spatial dimensions and channel dimensions, making full use of the correspondence between high-level features and low-level features. We conducted a series of experiments on two challenging urban road scene datasets (Cityscapes and CamVid) and achieved excellent results. Among them, the experimental results on the Cityscapes dataset show that for 512 x 1024 high-resolution images, our method achieves 71.62% Mean Intersection over Union (mIoU) with 109.9 frames per second (FPS) on a single 1080Ti GPU.