%A Van Viet Doan
%A Duy Hung Nguyen
%A Quoc Long Tran
%A Do Van Nguyen
%A Thanh Ha Le
%T Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks
%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
%C Toyama, Japan
%D 2018
%L SisLab3263