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Real-time defects detection system for orange citrus fruits using multi-spectral imaging

机译:多光谱成像的柑橘类水果实时缺陷检测系统

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In this paper, we propose a computer vision system that detects the external defects of orange citrus fruits using multi-spectral imaging sensor. First, the proposed algorithm segments the orange fruit from the captured Near-Infra Red (NIR) and RGB images using only the NIR component. Second, some adaptive pre-processing techniques are applied on the segmented RGB and NIR orange fruit images. Hence, a thresholding technique is utilized in order to detect the defects in seven different color components of the orange fruit. Finally, a voting process is applied on the seven thresholded color components images to determine if the citrus fruit image is defected or defect free. The overall accuracy of the algorithm is more than 95%, and the proposed algorithm can process three images of resolution (640×480 pixels) per second.
机译:在本文中,我们提出了一种计算机视觉系统,该系统使用多光谱成像传感器检测橙柑桔的外部缺陷。首先,提出的算法仅使用NIR分量从捕获的近红外(NIR)和RGB图像中分割橙色水果。其次,将一些自适应预处理技术应用于分割后的RGB和NIR橙色水果图像。因此,为了检测橙果的七个不同颜色成分中的缺陷,使用了阈值技术。最后,对七个阈值颜色分量图像进行投票处理,以确定柑橘类水果图像是否有缺陷或无缺陷。该算法的整体准确率超过95%,并且该算法每秒可以处理三张分辨率为640×480像素的图像。

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