摘要
提出了一种基于Zernike矩改进的亚像素边缘提取的工件缺陷检测算法。对图像进行小波分解,并对分解的各频段信息分别利用不同算法进行预处理,重构图像后可以有效地滤除图像噪声,增强目标信息;利用改进的Zernike矩亚像素边缘提取算法定位图像边缘并提取特征信息,减小了边缘信息误差,能够更精确地分割出目标轮廓;通过计算连通区域几何参数及全局信息熵来判断是否存在缺陷。通过实验对算法进行了验证,结果表明,提出的算法可以降低金属高光噪声,有效地提取缺陷边缘,并且在环境光照变化时具有较强的稳健性,金属工件的缺陷检测精度得到提高。
This study proposes a novel subpixel edge extraction algorithm for the detection of defects in workpieces,which is based on Zernike moments.First,the target image is decomposed using a wavelet transform,and the decomposed frequency information is preprocessed by employing different algorithms.After reconstruction,the image noise can be effectively filtered out and the target information can be enhanced.Then,the proposed subpixel edge extraction algorithm is applied to locate the image edges and extract their feature information with the aim to reduce the edge information error and segment the target contour more accurately.Finally,the geometric parameters of the surrounding region and the global information entropy are calculated to determine whether there are defects.The algorithm is verified with an experiment,and the experimental results show that the proposed algorithm can reduce the metal high-light noise and extract the defect edges effectively.Moreover,the algorithm is robust even when the ambient light illumination changes,and thus improves the accuracy of metal-defect detection.
作者
刘婷婷
王培光
张娜
Liu Tingting;Wang Peiguang;Zhang Na(College of Electronic Information Engineering,Hebei University,Baoding,Hebei 071002,China;College of Physical Science and Technology,Hebei University,Baoding,Hebei 071002,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第12期122-129,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(11271106)
河北省教育厅青年基金(072135142)
关键词
测量
高光去噪
缺陷检测
ZERNIKE矩
亚像素边缘提取
三维块匹配滤波算法
measurement
high-light denoising
defect detection
Zernike moment
subpixel edge extraction
block matching and three-dimensional filtering algorithm
