摘要
针对目前钢球表面反射率高、缺陷特征难以提取等问题,提出了一种基于机器视觉技术,利用钢球表面激光散射图片快速检测表面缺陷的方法。介绍了系统的结构和检测原理,搭建了系统检测平台。分析了利用形态学、边缘连接等图像处理算法提取缺陷散斑,利用圆度因子和延长因子判别缺陷类型的处理过程,并基于Lab VIEW软件平台,选取不同缺陷的钢球样本进行了试验测试。实际试验表明,利用钢球表面激光散射图可以有效提取表面凹坑、麻点、刻痕等缺陷信息,系统可以快速准确地识别钢球的表面缺陷,可用于钢球表面缺陷的在线检测,具有很好的实用价值和应用前景。
As the surface of steel ball has high reflectivity and defect feature is difficult to extract,a rapid surface defect detection method is proposed which is based on the laser scattering image of steel ball surface and machine vision technology. The detecting system is designed,and the structure and principle of the detecting system are introduced.The image processing algorithms of morphology and edge connecting are used to extract laser speckles of the defects,and Heywood circularity factor and elongation factor are used to judge the defect type. Based on the LabVIEW software platform,steel ball samples of different defects are tested. Test results show that the defects which include pits,hard spots and scratches can be effectively extracted by using laser scattering images of steel ball surface. Based on the integrated system,the defects on the steel ball surface can be recognized rapidly and exactly. The system has practical value and application prospect for on-line testing of steel ball surface defects.
出处
《激光与红外》
CAS
CSCD
北大核心
2015年第11期1304-1308,共5页
Laser & Infrared
基金
国家自然科学基金青年基金项目(No.61302181)
闵行区产学研项目(No.3714302006)
上海市教委科研创新项目(No.13YZ111)
上海市自然科学基金(No.14ZR1418400)资助
关键词
激光散斑
机器视觉
钢球
缺陷检测
laser speckle
machine vision
steel ball
defect detection
