关键词: 脐橙, 表面缺陷, 分割, 去除背景, 亮度校正, 单阈值

Abstract:

【Objective】 The purpose of this study was to effectively solve the problem that some defects of fruit images with defective peels were mistakenly divided into backgrounds when removing background, and it was difficult to effectively segment and extract fruit surface defects.【Method】 Taking Newhall navel orange as the research object, this paper proposed to remove the background based on HSI color space model method to construct the mask template with I component image, and to select a single threshold (T=75) by bimodal method according to its gray histogram information and filled the holes to obtain a mask template. At last, the mask template and I component image were obtained by dot multiplication to obtain I component image from which the background was removed. A multi-scale Gaussian function image brightness correction algorithm was proposed to correct the brightness of I component image after removing the background. By constructing a multi-scale Gaussian function filter, I component image with the background removed and the constructed multi-scale Gaussian function filter were convoluted to obtain the surface illumination component image of I component image after the background was removed. Finally, the I component image after removing the background and the obtained illumination component image were subjected to dot division operation to obtain a luminance correction image of the I component image after the background was removed. At last, the surface defects of navel orange were extracted by a single global threshold method.【Result】 The background was removed based on the HSI color space model method, and the surface information of the navel orange could be preserved while the background was effectively removed, which was beneficial to subsequent operations. The image brightness correction algorithm based on multi-scale Gaussian function was used to extract the defects of the six common navel orange defects, and then the single-threshold method was used to extract the defects. Therefore the surface defects of navel oranges with different gray levels were successfully segmented at one time, and the segmentation rate was up to 100%, the lowest was 88.5%, and the total was 92.7%. Through experimental analysis, it was found that the cause of partial mis-segmentation or leakage segmentation was mainly due to the fact that some defects were lighter in color, and the difference in gray level from normal region was smaller, resulting in leakage segmentation. There were still some defects due to the small defect area, which was mistaken for noise removal during image morphology processing. At the same time, the false positive rate of normal fruit was also found to be 10.8%. It was found that the fold of a part of the normal fruit epidermal tissue area was located in the edge area of the image, which was mistaken for the defect of the edge area, resulting in misjudgment.【Conclusion】 The experimental results showed that image removal based on HSI color space model and image brightness unevenness correction algorithm based on multi-scale Gaussian function had achieved good results for background image segmentation of Newhall navel orange image and I component image surface brightness correction after background removal. It provided technical support for the precise grading of navel oranges and also provided a new idea for the rapid detection of other fruit surface defects.

Key words: navel orange, surface defect, segmentation, remove background, brightness correction, single threshold