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
2020 Sensors  
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
more » ... is 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.
doi:10.3390/s20102907 pmid:32455537 fatcat:tmlnyjqzivbibeqklgyot3bxdm