中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2018, Vol. 37 ›› Issue (03): 92-98.DOI: 10.3969/j.issn.1674-0696.2018.03.16

• 交通运输工程 • 上一篇    下一篇

基于双目视觉图像的倒车障碍物检测预处理方法

刘昱岗1,2,王卓君1,刘艳芳1,张祖涛3,徐宏4   

  1. (1.西南交通大学 交通运输与物流学院,四川 成都 610031; 2.西南交通大学 综合交通运输智能化国家 地方联合工程实验室,四川 成都 610031; 3.西南交通大学 机械工程学院,四川 成都 610031; 4.中国电子科技集团第三十二研究所,上海 201808)
  • 收稿日期:2016-08-01 修回日期:2016-11-11 出版日期:2018-03-20 发布日期:2018-03-26
  • 作者简介:刘昱岗(1978—),男,湖南株洲人,副教授,主要研究方向为智能交通。E-mail:liuyugang@home.swjtu.edu.cn。

Reversing Obstacle Detection Based on Binocular Vision Image

LIU Yugang1,2, WANG Zhuojun1, LIU Yanfang1, ZHANG Zutao3, XU Hong4   

  1. (1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, P. R. China; 2. National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, Sichuan, P. R. China; 3. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, P. R. China; 4. Thirty-second Research Institute of China Electronic Science and Technology Group, Shanghai 201808, P. R. China)
  • Received:2016-08-01 Revised:2016-11-11 Online:2018-03-20 Published:2018-03-26

摘要: 针对倒车过程中双目视觉图像障碍物检测时的误匹配、障碍物检测范围等问题,提出基于双目视觉图像的倒车障碍物检测方法。利用膨胀和腐蚀操 作来消除误匹配及部分噪声;采用视差图像的二值化处理来控制障碍物的检测范围,实现基于距离的障碍物检测;接着使用基于行程的连通区域标记算法 对各连通区域进行标记;最后计算各区域面积,将面积过小的区域作为噪点去除,将剩余区域作为障碍物提出来。实验结果表明笔者提出的基于双目视觉 图像的倒车障碍物检测方法能够有效检测出车后图像的障碍物区域。

关键词: 交通运输工程, 智能交通, 双目视觉, 倒车, 区域标记, 障碍物检测

Abstract: According to the problem of false matching and obstacle detection range of binocular vision image obstacle detection in the course of reversing, a reversing obstacle detection method based on binocular stereo vision was proposed. Firstly, dilation and erosion algorithms were implemented in the disparity map to connect discontinuous region and remove small noise points. Binaryzation processing of parallax images was adopted to control the detection range of obstacles, which realized the detection of obstacles based on distance. Then the connectivity region marking algorithm based on travel was used to mark all connected regions. Finally, the area of the above regions was calculated. The region with too small area was removed as hot pixel, and the remaining area was proposed as an obstacle. The experiment results demonstrate that the proposed detection method of reversing obstacle based on binocular vision image can effectively detect the obstacle area of the post-car image.

Key words: traffic and transportation engineering, intelligent transportation, binocular vision, reversing, region labelling, obstacle detection

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