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Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time

机译：实时自动检测无花果干质量的计算机视觉

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摘要

This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product. (c) 2015 Elsevier B.V. All rights reserved.
机译：这项工作报告了基于计算机视觉的自动化系统的开发，以改善对Cosenza干无花果（受保护的原产地）的质量控制和分类，重点是两个研究问题。第一种是通过颜色评估对无花果的定性判断，将使用数码相机获得的彩色图像的分析与根据常规仪器方法（即实验室目前进行的比色法）获得的图像进行比较。数据以CIE XYZ，CIELAB和HunterLab颜色空间以及每种水果的褐变指数测量值表示，然后使用基于PCA和PLS-DA的方法进行分析。结果表明，根据颜色属性，色度计和图像分析都可以完全区分高质量和无花果。第二个研究目的是开发图像处理算法，以使用基于机器视觉的实验原型模拟工业应用来实现无花果的实时排序。正确分类了极高的99.5％退化无花果和89.0％浅色优质无花果。深色优质无花果获得的百分率较低，但结果是可以接受的，因为大多数混淆都属于两类优质产品。 （c）2015 Elsevier B.V.保留所有权利。

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