基于视觉的金属成形工件尺寸和缺陷检测
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摘要
针对金属成形工件存在成形尺寸误差和外形缺陷,使用人工抽检效率低,不能对全部工件进行检测,大量存在未检出残次品交于客户的问题,提出一种基于HALCON视觉软件的金属成形工件尺寸与外形缺陷检测的方案。该方案基于金属成形工件形态学和灰度值特征,运用边缘提取和圆拟合算法,精确检测金属成形工件内圆孔尺寸。应用阈值分割与基于形状的标准模板匹配算法对金属成形工件进行区域匹配,检测工件外形缺陷。分别采集50个金属成形工件的图像并对其进行上述方法的检测,工件尺寸的检测正确率98. 5%,外形缺陷件检测正确率98%。
For metal forming workpieces,there are errors in forming dimensions and forming defects. The efficiency of manual sampling inspection is low,detection accuracy is low,and there is a problem that undetectable defective products are delivered to customers. A metal forming workpiece dimension based on HALCON vision software is studied. The method is based on the morphological and gray-value characteristics of metal forming workpieces. The edge extraction and circle fitting algorithm is used to precisely detect the size of round holes in metal forming workpieces. Threshold segmentation and shapebased standard template matching algorithm are used to achieve rapid matching of the target area and shape contour defect detection. The 50 metal forming workpiece images are examined respectively. The accuracy of the metal forming workpiece size is detected to be 98. 5%,and the accuracy of the external defect detection is 98%.
For metal forming workpieces,there are errors in forming dimensions and forming defects. The efficiency of manual sampling inspection is low,detection accuracy is low,and there is a problem that undetectable defective products are delivered to customers. A metal forming workpiece dimension based on HALCON vision software is studied. The method is based on the morphological and gray-value characteristics of metal forming workpieces. The edge extraction and circle fitting algorithm is used to precisely detect the size of round holes in metal forming workpieces. Threshold segmentation and shapebased standard template matching algorithm are used to achieve rapid matching of the target area and shape contour defect detection. The 50 metal forming workpiece images are examined respectively. The accuracy of the metal forming workpiece size is detected to be 98. 5%,and the accuracy of the external defect detection is 98%.
引文
[1]黄柳倩.基于机器视觉的冲压件缺陷检测系统研究[D].广州:广东工业大学,2012.
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[3]陈艺峰.HALCON在工件二维尺寸检测上的应用[J].机电技术,2011,34(4):12-13.
[4]王梅,梁锦华.蜂窝纸芯成形质量在线检测系统的开发[J].包装工程,2013,34(23):88-91.
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[6]彭飞,陈维荣,冒波波,等.基于Canny边缘检测和聚合接续法的路轨边缘提取方法[J].铁道学报,2012,34(2):52-57.
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[9]Sonka M,Hlavac V,Ceng R B D M.Image processing,analysis and machine vision[J].Journal of Electronic Imaging,2014(82):685-686.
[2]何金彪,周永霞.基于机器视觉的罐盖缺陷检测[J].中国计量学院学报,2015,26(3):269-273.
[3]陈艺峰.HALCON在工件二维尺寸检测上的应用[J].机电技术,2011,34(4):12-13.
[4]王梅,梁锦华.蜂窝纸芯成形质量在线检测系统的开发[J].包装工程,2013,34(23):88-91.
[5]孙怀远,廖跃华,周夫之,等.基于HALCON的药品包装瓶批号检测技术研究[D]上海:上海理工大学,2008.
[6]彭飞,陈维荣,冒波波,等.基于Canny边缘检测和聚合接续法的路轨边缘提取方法[J].铁道学报,2012,34(2):52-57.
[7]李渊,乔兵,陆宇平.基于聚类方法的圆拟合算法[J].计算机应用,2013,33(22):206-208.
[8]张瑾,高军,付衍文.一种基于改进动态阈值的缺陷提取算法[J].包装工程,2012,33(11):107-111.
[9]Sonka M,Hlavac V,Ceng R B D M.Image processing,analysis and machine vision[J].Journal of Electronic Imaging,2014(82):685-686.