Edge guided Hierarchically Nested Network for Real time Semantic Segmentation
Semantic segmentation requires both large region context information and rich spatial information. The former can be achieved by a deep enough network structure. However, most spatial information lost in repeated down sample operation in deep Convolutional Neural Networks cannot be easily recovered. In this paper, we propose a novel edge guided hierarchically nested network to improve both speed and accuracy of real time semantic segmentation. We introduce Edge Guidance Unit which aims to guide the decoder network to exploit high resolution clues, recover and refine spatial information with low additional computational cost. We also introduce a well designed Fusion Block to combine different resolution features along with edge guidance. In experiments we demonstrate that our network stabilizes the small object region while achieving a result of 72.7% mean IoU at 40 FPS on CityScapes test dataset.
2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)