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.