基于掩模及边缘灰度补偿算法的脐橙背景及表面缺陷分割

缺陷检测一直是利用计算机视觉技术进行水果自动分级的难点。为了解决带有缺陷的水果在图像分割时部分缺陷容易被误分割为背景这一问题，以脐橙为研究对象，首先提取B分量，利用B分量构建掩模图像，然后对R分量图像进行掩模，从而在不损伤缺陷的情况下实现了水果与背景100%分割。考虑到水果呈球状，检测时边缘灰度较低，在缺陷分割时容易出现误分割，提出快速水果图像边缘灰度补偿算法，利用此算法，对6种常见脐橙缺陷，共计220幅图像，设定分割阈值为165，使不同灰度等级的缺陷一次性分割成功，分割率最高为100%，最低为79.5%。试验结果表明由于单阈值的使用，提高了缺陷分割效率。

Background and external defects segmentation of navel orange based on mask and edge gray value compensation algorithm

Detection of fruits surface defects is always a challenging project for automated fruit grading of computer vision. A new method was developed to solve the problem of a part of defects easily being mistaken for the background when the fruits with defects were segmented from the background. First the B-component image of navel orange was extracted and built mask, then R-component image was masked by B-component image, thus 100% fruits and background segmentation with intact defects was achieved. Considering false segmentation of defects owing to the lower edge gray values of spherical fruits, an algorithm of fast fruit image edge gray value compensation was advanced. Using this algorithm, six kinds of common defects of navel orange, for a total of 220 images, setting the threshold for 165, the defects of different gray value was successfully segmented at one time. The highest segmentation rate was 100% and the lowest was 79.5%. Test results showed that the segmentation efficiency of the defects was improved as a result of the use of a single threshold.