摘要
钢板表面缺陷严重降低钢板的耐磨性、耐高温性、耐腐蚀性、抗疲劳强度等性能,因此,钢板表面缺陷的检测就显得尤为重要。本文基于机器视觉采用Matlab图像处理技术对钢板表面缺陷进行检测识别。在不同光照条件下采集钢板表面图像,分别进行图像处理,讨论分析不同光照条件和去噪方法对检测结果的影响。首先对缺陷图像进行预处理,然后将预处理后的图像二值化及形态学图像处理,使图像背景与对象图形分离,提取出表面缺陷特征,计算缺陷的面积和周长。通过对图像细化和骨架提取线性缺陷,计算出缺陷长度,并且通过对像素的标定,将像素单位转化为长度或面积单位。实验结果表明该方法具有很好的可靠性和重复性。
Steel plate surface defects seriously reduce the steel wear resistance,high temperature resistance,corrosion resistance,fatigue resistance and other properties.Therefore,the detection of plate surface defects is very important.This paper proposes a new method to detect steel defects based on machine vision.Collecting images of steel plate surface in various light conditions are discussed.Firstly,the defect images are preprocessed,and then the preprocessed images are changed to binary images and are processed morphologically.Finally,the image background and object graphics are separated,and the surface defect features are extracted to calculate the defect area and perimeter.After the calibration,the defect’s area and length can be obtained.The experimental results show that the method is of reliability and repeatability.
出处
《计算机与现代化》
2013年第7期130-134,共5页
Computer and Modernization
关键词
机器视觉
钢板表面
缺陷检测
machine vision
steel plate surface
defects detecting
