雾天环境下机器视觉的边坡监测方法

doi:10.13474/j.cnki.11-2246.2022.0177.

测绘通报 ›› 2022, Vol. 0 ›› Issue (6): 88-92.doi: 10.13474/j.cnki.11-2246.2022.0177.

雾天环境下机器视觉的边坡监测方法

时梦杰, 陶庭叶, 高飞, 李江洋, 陈皓

合肥工业大学土木与水利工程学院, 安徽合肥 230009

收稿日期:2021-07-15 发布日期:2022-06-30
作者简介:时梦杰(1997-),男,硕士生,主要研究方向为机器视觉、变形监测。E-mail:983562974@qq.com
基金资助:
煤炭资源清洁利用与矿山环境保护湖南省重点实验室开放研究基金(E22015)

Slope monitoring method based on machine vision in foggy weather

SHI Mengjie, TAO Tingye, GAO Fei, LI Jiangyang, CHEN Hao

College of Civil Engineering, Hefei University of Technology, Hefei 230009, China

Received:2021-07-15 Published:2022-06-30

摘要/Abstract

摘要： 针对在机器视觉的边坡监测过程中,雾的存在会降低图像质量,影响监测效果的问题,本文提出了一种结合暗通道先验(DCP)的边坡监测方法。首先,通过计算实时图像的FADE值,判断采集的图像是否需要去雾。对于需要去雾的图像,利用DCP算法进行去雾处理,还原其纹理细节信息;然后,利用尺度不变特征变换(SIFT)算法对去雾后的图像进行特征点匹配,利用随机抽样一致性(RANSAC)算法筛选优秀的匹配点对,获取模板与图像的变换矩阵,进而利用仿射变换求取模板坐标,求得边坡的位移。试验结果表明,本文方法在不同浓度的雾霭图像下均表现良好,有效克服了视觉监测在雾天环境难以应用的问题。

关键词: 暗通道先验, 机器视觉, 边坡监测, 位移提取, 尺度不变特征变换

Abstract: In order to solve the problem that the presence of fog will reduce the image quality and affect the monitoring effect in the process of slope monitoring by machine vision, so a slope monitoring method combining dark channel prior(DCP) is proposed. Firstly, by calculating the FADE value of the real-time image, which determines whether the collected image needs to be defogged. For images that need to be defogged, DCP algorithm is used for defogging processing to restore the texture details. Then, the scale-invariant feature transform(SIFT) algorithm is used to match the feature points of the image after fog removal, and the random sample consensus(RANSAC) algorithm is used to screen the excellent matching point pairs, and the transformation matrix between the template and the image is obtained. Then, the affine transformation is used to obtain the template coordinates and the slope displacement. The experimental results show that the proposed method performs well in fog images of different concentrations, and overcomes the problem that visual monitoring is difficult to apply in fog environment effectively.

Key words: DCP, machine vision, slope monitoring, displacement extract, SIFT

中图分类号:

P208

时梦杰, 陶庭叶, 高飞, 李江洋, 陈皓. 雾天环境下机器视觉的边坡监测方法[J]. 测绘通报, 2022, 0(6): 88-92.

SHI Mengjie, TAO Tingye, GAO Fei, LI Jiangyang, CHEN Hao. Slope monitoring method based on machine vision in foggy weather[J]. Bulletin of Surveying and Mapping, 2022, 0(6): 88-92.

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雾天环境下机器视觉的边坡监测方法

Slope monitoring method based on machine vision in foggy weather

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