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Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques

机译:结合短波红外高光谱成像,校准和眩光校正技术,基于实时像素的早期苹果瘀伤检测

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摘要

High speed data processing for online food quality inspection using hyperspectral imaging (HSI) is challenging as over hundred spectral images have to be analyzed simultaneously. In this study, a real-time pixel based early apple bruise detection system based on HSI in the shortwave infrared (SWIR) range has been developed. This systems consists of a novel, homogeneous SWIR illumination unit and a line scan camera. The system performance was tested on Jonagold apples bruised less than two hours before scanning. Partial least squares-discriminant analysis was used to discriminate bruised pixel spectra from sound pixel spectra. As the glossiness of many fruit and vegetables limits the accuracy in the detection of defects, several reflectance calibrations and pre-processing techniques were compared for glare correction and maximizing the signal to noise ratio. With the best combination of first derivative and mean centering, followed by image post-processing, this system was able to detect fresh bruises in thirty apples with 98% accuracy at the pixel level with a processing time per apple below 200 ms. (C) 2016 Elsevier Ltd. All rights reserved.
机译:使用高光谱成像(HSI)进行在线食品质量检查的高速数据处理颇具挑战性,因为必须同时分析一百多个光谱图像。在这项研究中,基于短波红外(SWIR)范围内基于HSI的实时基于像素的早期苹果瘀伤检测系统已经开发出来。该系统由新颖的均匀SWIR照明单元和线扫描相机组成。扫描前不到两个小时,系统就在瘀伤的乔纳金苹果上测试了系统性能。偏最小二乘判别分析用于区分瘀伤像素光谱与声音像素光谱。由于许多水果和蔬菜的光泽度限制了缺陷检测的准确性,因此对几种反射率校准和预处理技术进行了比较,以进行眩光校正并最大程度地提高信噪比。借助一阶导数和平均居中的最佳组合,然后进行图像后处理,该系统能够在像素级别以98%的准确度检测30个苹果中的新鲜瘀伤,每个苹果的处理时间低于200 ms。 (C)2016 Elsevier Ltd.保留所有权利。

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