Efficient Fast Semantic Segmentation Using Continuous Shuffle Dilated Convolutions

It is difficult for many semantic segmentation methods to perform useful inferences under extremely resource-constrained devices; therefore, an efficient fast semantic segmentation network (EFSNet) that employs a continuous shuffle dilated convolution (CSDC) and a up-sampling module is proposed in this paper. First, the number of parameters is reduced by group convolutions and the receptive field is enlarged by dilated convolution. Second, a up-sampling module is proposed for reducing noise and increasing inference speed. Finally, with the above CSDC module and up-sampling module, the EFSNet based on a fast semantic segmentation network is obtained. Our experiments on the CamVid and Cityscapes datasets show that the mean intersection over union (mIoU) values obtained by the proposed EFSNet with only 173 k parameters are 61.1% and 61.9%, respectively. The method is able to run in real-time at a speed of 332 frames per second (FPS) and 107 FPS on a single NVIDIA Titan Xp GPU. Compared with several existing methods, our network achieves efficient segmentation with low resource consumption.