Abstract
The semantic segmentation of low altitude high-resolution urban scene images taken by UAV plays an important role in city management. However, such images have the characteristics of inter-class homogeneity and intra-class heterogeneity. How to segment these images quickly and accurately is still challenging. In this paper, we propose a novel double-branch network. For the challenge of inter-class homogeneity, a boundary flow module is designed to enhance the flow of latent semantic information between two branches by imposing boundary constraints between classes. To alleviate intra-class heterogeneity, a context extraction module based on adaptive dynamic fusion is designed, which effectively captures the long-term relationship of features with very low parameters. Experiments on two typical datasets show that our approach achieves the best balance between accuracy and speed. Specifically, we achieve 65.8% mIoU and 74.1% mIoU on UAVid test set and UDD validation set respectively, and 60FPS on an NVIDIA TITAN Xp.
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