TY  - CONF
ID  - SisLab3263
UR  - http://scis2018.j-soft.org/program.html
A1  - Doan, Van Viet
A1  - Nguyen, Duy Hung
A1  - Tran, Quoc Long
A1  - Nguyen, Do Van
A1  - Le, Thanh Ha
Y1  - 2018/12/05/
N2  - ?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
TI  - Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks
M2  - Toyama, Japan
AV  - public
T2  - 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
ER  -