机器视觉技术在黄花梨尺寸和果面缺陷检测中的应用

为提高出口水果品质，对黄花梨进行了机器视觉技术检测外形尺寸与表现状况的试验研究。通过确定图像处理窗口、利用Ｓｏｂｅｌ算子和Ｈｉｌｄｉｔｃｈ细化边缘；确定形心点找出代表果径，试验检测结果表明，预测果径值与实际尺寸的相关系数可达０．９６。对检测果面缺陷，提出利用红（Ｒ）、绿（Ｇ）色彩分量在坏损与非坏损交界处的突变，求出可疑点，再经区域增长定出整个受损面，试验对比表明该算法是精确的

Application of Machine Vision to Detecting Size and Surface Defect of Huanghua Pear

In view of the existing situation of fruit quality detection in China, which is still dependent on human sense organ to identify and judge the fruit, the broad application prospect of machine vision in quality evaluation of agricultural products, the method to detect the size and surface defect of Huanghua pear by machine vision were studied. The image processing region was decreased greatly by selecting suitable image processing window, and the thinned edge of pear was gained by use of Sober operator and Hilditch thinning algorithm. The maximum diameter, which is perpendicular to the line joined the center of pear and the intersection point of the stem and pear, was adopted to represent the size of pear, and it was found that the correlation coefficient of real size versus detected size was 0.96 . In the light of color difference in the joint area of the defected and non defected area, the light values of R(red) and G(green) were used to find the suspected defected area. The whole defected area was found by means of region growing method, and the area of surface defected was calculated finally. These results laid a solid foundation for further developing fruit quality detection system using machine vision.