Global-and-Local Context Network for Semantic Segmentation of Street View Images
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Department of Electrical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
2
Department of Computer Science & Information Engineering, National Central University, Taoyuan City 32001, Taiwan
3
Department of Computer Science, University Tunku Abdul Rahman, Kampar 31900, Malaysia
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(10), 2907; https://doi.org/10.3390/s20102907
Received: 12 February 2020
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Revised: 13 April 2020
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Accepted: 18 May 2020
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Published: 21 May 2020
(This article belongs to the Special Issue Image and Video Processing and Recognition Based on Artificial Intelligence)
Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Lin, C.-Y.; Chiu, Y.-C.; Ng, H.-F.; Shih, T.K.; Lin, K.-H. Global-and-Local Context Network for Semantic Segmentation of Street View Images. Sensors 2020, 20, 2907. https://doi.org/10.3390/s20102907
AMA Style
Lin C-Y, Chiu Y-C, Ng H-F, Shih TK, Lin K-H. Global-and-Local Context Network for Semantic Segmentation of Street View Images. Sensors. 2020; 20(10):2907. https://doi.org/10.3390/s20102907
Chicago/Turabian StyleLin, Chih-Yang, Yi-Cheng Chiu, Hui-Fuang Ng, Timothy K. Shih, and Kuan-Hung Lin. 2020. "Global-and-Local Context Network for Semantic Segmentation of Street View Images" Sensors 20, no. 10: 2907. https://doi.org/10.3390/s20102907
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