摘要
机器视觉对裂缝进行宽度测量时存在测量方向不能有效反映裂缝真实宽度方向、测量不精确的问题。为此,以水库大坝的裂缝为对象,主要研究了裂缝主干提炼和宽度测量方法。在图像细化的基础上进行进一步精简,得到裂缝的主干,其主干每点的八邻域总点数不超过2,精简了冗余的数据点,邻域分布种类数减至16种,增强了主干对裂缝形状的描述能力;融合主干宏观和微观特征作为宽度测量方向的依据,获得较对比方法更为准确的测量方向,实现裂缝宽度连续、准确的视觉测量。增加测量召回率与方向误差两种评估标准,全面地验证所提方法的准确性。所提方法具有实际工程应用的前景,并为其他细长不规则目标的径向视觉测量提供参考。
The width measurement of cracks by machine vision has problems of offsets in the width direction of the cracks and inaccurate measurements. To this end, taking the cracks of reservoir dams as objects, the method of crack backbone refining and width measurement was studied. Based on image thinning, the proposed method refined the cracks’ backbone further. The total number of points in the eight neighborhoods of each backbone’s point did not exceed two. Redundant data points were simplified, and the number of neighborhood distribution types was reduced to 16, which enhanced the backbone’s ability to describe the crack shape. The backbone’s macroscopic and microscopic features were combined as a basis for the width measurement direction to obtain a more accurate measurement direction than a baseline method used for comparison. As a result, the proposed method achieved continuous and accurate visual measurements of the crack width. By adding two evaluation criteria, measurement recall rate and direction error, the proposed method was validated to be more accurate in actual engineering requirements than the baseline method. The proposed method has practical engineering applications and can be a reference for radial vision measurement of other slender and irregular targets.
作者
黄钊丰
唐昀超
邹湘军
陈明猷
周浩
邹天龙
Huang Zhaofeng;Tang Yunchao;Zou Xiangjun;Chen Mingyou;Zhou Hao;Zou Tianlong(Collage of Engineering,South China Agriculture University,Guangzhou 510642,Guangdong,China;College of Urban and Rural Construction,Zhongkai University of Agriculture and Engineering,Guangzhou 510006,Guangdong,China;Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics,Foshan 528200,Guangdong,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第10期273-283,共11页
Laser & Optoelectronics Progress
基金
国家自然科学基金(31571568)
广东省省级科技计划(2019A050510035)
广东省普通高校省级重大科研项目(2020KZDZX1037)。
关键词
机器视觉
视觉测量
裂缝测量
图像细化
多尺度特征融合
machine vision
visual measurement
crack measurement
image thinning
multi-scale feature fusion
