针对基于机器视觉技术的水果表面缺陷因受到亮度不均影响而提取困难的问题,以阿克苏苹果为研究对象,采用可见-近红外双CCD成像系统,设计了一种无需预先建模的类球形亮度变换方法,对R分量图像进行亮度变换,变换后的图像使整个水果表面正常区域灰度趋于一致,而缺陷区域依然保留为低灰度区,增强了缺陷和正常果皮的对比度,提高了缺陷检测精度.使用共计100个样本评估算法的可行性,其中45个缺陷果的检测精度为93.3％,55个正常果的检测正确率为100％,整体检测精度达到97％.研究结果表明,利用基于类球形亮度变换结合单阈值分割方法提取水果表面缺陷是可行的.%The non-uniform intensity distribution on the fruit's images is the main reason resulting in the difficulty and low accuracy of surface defects detection by using a machine vision system. A detection system based on Vis-NIR double CCDs was built for detecting surface defects on 'Akesu' apples. A spherical intensity transformation method ( SITM) was proposed to transform the R channel image of an apple, which enhanced the intensity uniformity of the normal regions and kept the low intensity of the defected regions in an apple. The intensity contrast between the defect regions and those of normal tissue was also improved, which increased the defect detection accuracy. A defect detection algorithm was developed based on the SITM and 100 apples consisting of 45 defected apples and 55 intact apples were used to evaluate the performance of the algorithm. Results showed that 93. 3% of defected apples were correctly classified and 100% of the intact apples were correctly recognized. The overall detection accuracy was 97% . It is feasible to extract the surface defects on apples using the proposed SITM combining with a single threshold segmentation method.
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