%0 Conference Paper
%A Doan, Van Viet
%A Nguyen, Duy Hung
%A Tran, Quoc Long
%A Nguyen, Do Van
%A Le, Thanh Ha
%B 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and  19th International Symposium on Advanced Intelligent Systems  in conjunction with Intelligent Systems Workshop
%C Toyama, Japan
%D 2018
%F SisLab:3263
%T Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3263/
%X —Semantic image segmentation plays a key role in  obtaining pixel-level understanding of images. In recent years,  researchers have tackled this problem by using deep learning  methods instead of traditional computer vision methods (eg [25]).  Because of the development of technologies like autonomous  vehicles and indoor robots, segmentation techniques, that have  not only high accuracy but also the capability of running in  real-time on embedded platform and mobile devices, are in high  demand. In this work, we have proposed a new convolutional  module, named Residual depth-wise separable, and a fast and  efficient convolutional neural network for segmentation. The  proposed method is compared against other state of the art  real-time models. The experiment results illustrate that our  method is efficient in computation while achieves state of the  art performance in term of accuracy