  Real-time semantic segmentation plays a significant role in industry
applications, such as autonomous driving, robotics and so on. It is a
challenging task as both efficiency and performance need to be considered
simultaneously. To address such a complex task, this paper proposes an
efficient CNN called Multiply Spatial Fusion Network (MSFNet) to achieve fast
and accurate perception. The proposed MSFNet uses Class Boundary Supervision to
process the relevant boundary information based on our proposed Multi-features
Fusion Module which can obtain spatial information and enlarge receptive field.
Therefore, the final upsampling of the feature maps of 1/8 original image size
can achieve impressive results while maintaining a high speed. Experiments on
Cityscapes and Camvid datasets show an obvious advantage of the proposed
approach compared with the existing approaches. Specifically, it achieves 77.1%
Mean IOU on the Cityscapes test dataset with the speed of 41 FPS for a
1024*2048 input, and 75.4% Mean IOU with the speed of 91 FPS on the Camvid test
dataset.
