Obstacle detection in single images with deep neural networks

被引：26

作者：

Jia, Baozhi ^{[1
]}

Feng, Weiguo ^{[1
]}

Zhu, Ming ^{[1
]}

机构：

[1] Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China

来源：

SIGNAL IMAGE AND VIDEO PROCESSING | 2016年 / 10卷 / 06期

关键词：

Autonomous navigation; Obstacle detection; Single image; Deep neural network and obstacle depth;

D O I：

10.1007/s11760-015-0855-4

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Obstacle detection in single images is a challenging problem in autonomous navigation on low-cost condition. In this paper, we introduce an approach for obstacle detection in single images with deep neural networks. We propose the followings: (1) a deep model combined with other deep neural network for obstacle detection; (2) a method to segment obstacles and infer their depths. Among others, both local and global information are generated in our method for better classification and portability. Experiments are performed on the open datasets and images captured by our autonomous vehicle. The results show that our method is effective in both obstacle detection and depth inference.

引用

页码：1033 / 1040

页数：8

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