Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2825-2829,2871

Road scene understanding for autonomous driving via deep residual learning

Song Rui1a
Shi Zhiping1a
Qu Ying2
Shao Zhenzhou1b
Guan Yong1b
1. a. Beijing Advanced Innovation Center for Imaging Technology, b. Beijing Key Laboratory of Light Industrial Robot & Safety Verification, College of Information Engineering, Capital Normal University, Beijing 100048, China
2. Dept. of Electrical Engineering & Computer Science, University of Tennessee-Knoxville, Tennessee 37996, USA
DOI: 10.19734/j.issn.1001-3695.2018.03.0234

Abstract

It is making great progress in the autonomous driving field with the rapid development of road scene understanding techniques. The safety is a concerning issue with respect to the real-time and accurate performance in the related tasks which contains the road segmentation, road classification and vehicle detection. To this end, this paper proposed an approach based on deep residual learning with an encoder-decoder network structure. On the one hand, the encoder network structure used different layers of residual networks to extract the abstract features in the high dimension, which shared in the next three tasks. On the other hand, the decoder network structure adopted a mechanism of parallel computing for sub-tasks, i. e., executed the road segmentation, vehicle detection and road classification tasks simultaneously. Additionally, it used the fully convolutional networks to upsample the extracted features to specifically solve the problem of road segmentation. At last, the experimental results show that the processing rate can effectively reach more than 15 fps with the high accuracy guaranteed.

Foundation Support

国家自然科学基金资助项目(61702348,61772351,61572331,61472468,61602325)
国家科技支撑计划资助项目(2015BAF13B01)
国际科技合作计划项目(2011DFG13000)
北京市科委项目(Z141100002014001)
北京市属高等学校创新团队建设与教师职业发展计划项目(IDHT20150507)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0234
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: Technology of Graphic & Image
Pages: 2825-2829,2871
Serial Number: 1001-3695(2019)09-058-2825-05

Publish History

[2019-09-05] Printed Article

Cite This Article

宋锐, 施智平, 渠瀛, 等. 基于深度残差学习的自动驾驶道路场景理解 [J]. 计算机应用研究, 2019, 36 (9): 2825-2829,2871. (Song Rui, Shi Zhiping, Qu Ying, et al. Road scene understanding for autonomous driving via deep residual learning [J]. Application Research of Computers, 2019, 36 (9): 2825-2829,2871. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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