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Assigning a label to each pixel in an image, namely semantic segmentation,has been an important task in computer vision, and has applications inautonomous driving, robotic navigation, localization, and scene understanding.Fully convolutional neural networks have proved to be a successful solution forthe task over the years but most of the work being done focuses primarily onaccuracy. In this paper, we present a computationally efficient approach tosemantic segmentation, while achieving a high mean intersection over union(mIOU), 70.33% on Cityscapes challenge. The network proposed is capable ofrunning real-time on mobile devices. In addition, we make our code and modelweights publicly available.

An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions