Global-and-local context network for semantic segmentation of street view images

Chih Yang Lin, Yi Cheng Chiu, Hui Fuang Ng, Timothy K. Shih, Kuan Hung Lin

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish
Article number2907
JournalSensors (Switzerland)
Issue number10
StatePublished - 2 May 2020


  • Fully convolutional networks
  • Global context
  • Local context
  • Semantic segmentation


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