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Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks

Doan, Van Viet and Nguyen, Duy Hung and Tran, Quoc Long and Nguyen, Do Van and Le, Thanh Ha (2018) Real-time Image Semantic Segmentation Networks with Residual Depth-wise Separable Blocks. In: 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, 5-8 December 2018, Toyama, Japan.

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—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

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Faculty of Information Technology (FIT)
Depositing User: Long Trần Quốc
Date Deposited: 14 Dec 2018 02:41
Last Modified: 14 Dec 2018 02:41

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