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结合立体视觉与超像素技术的道路障碍物概率检测

吴宗胜 李红 韩改宁

吴宗胜, 李红, 韩改宁. 结合立体视觉与超像素技术的道路障碍物概率检测[J]. 机械科学与技术, 2019, 38(2): 277-282. doi: 10.13433/j.cnki.1003-8728.20180288
引用本文: 吴宗胜, 李红, 韩改宁. 结合立体视觉与超像素技术的道路障碍物概率检测[J]. 机械科学与技术, 2019, 38(2): 277-282. doi: 10.13433/j.cnki.1003-8728.20180288
Wu Zongsheng, Li Hong, Han Gaining. Probability Detection of Road Obstacles Combining with Stereo Vision and Superpixels Technology[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(2): 277-282. doi: 10.13433/j.cnki.1003-8728.20180288
Citation: Wu Zongsheng, Li Hong, Han Gaining. Probability Detection of Road Obstacles Combining with Stereo Vision and Superpixels Technology[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(2): 277-282. doi: 10.13433/j.cnki.1003-8728.20180288

结合立体视觉与超像素技术的道路障碍物概率检测

doi: 10.13433/j.cnki.1003-8728.20180288
基金项目: 

陕西省教育厅专项科研计划项目 16JK1823

咸阳师范学院专项科研基金项目 XSYK18012

陕西省科技厅自然科学基础研究计划面上项目 2017JM6086

陕西省教育厅专项科研计划项目 17JK0826

详细信息
    作者简介:

    吴宗胜(1974-), 讲师, 博士研究生, 研究方向为智能机器人和机器视觉, wuzs2005@163.com

  • 中图分类号: TP301

Probability Detection of Road Obstacles Combining with Stereo Vision and Superpixels Technology

  • 摘要: 针对道路障碍物检测问题,提出了一种基于立体视觉与超像素技术的双目障碍物概率检测算法。该算法首先利用双目相机采集的左右视图进行立体匹配获取视差图,通过三维重建到得3D点云,然后采用最小二乘法拟合地平面方程,并计算图像空间点到地平面的距离,接着使用SLIC算法得到超像素,并计算超像素到地平面的中值距离,最后使用逻辑回归方法和信任函数计算像素为障碍物的概率。实验结果表明,该方法能够可靠地检测道路障碍物,并为障碍物检测的多传感器融合方法提供有价值的概率数据。
  • 图  1  基于立体视觉的检测障碍物检测概率

    图  2  有关到地平面距离的非障碍物的概率、可信概率和似然概率

    图  3  由超像素到地平面的最小距离和最大距离得出的不确定概率

    图  4  部分实验效果图

    表  1  不同概率类型准确率和召回率检测结果

    概率类型 准确率/% 召回率/%
    中值概率 84.5 90.4
    可信概率 88.6 86.2
    似然概率 82.3 92.8
    下载: 导出CSV

    表  2  实验对比结果

    方法 准确率/% 召回率/%
    单目方法[2] 76.7 88.2
    双目方法[7] 84.3 86.8
    本文方法 87.8 85.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-08-15
  • 刊出日期:  2019-02-05

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