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Double-channel on-line automatic fruit grading system based on computer vision

机译:基于计算机视觉的双通道在线自动果实分级系统

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The technology of fruit grading based on computer vision was studied and a double-channel on-line automatic grading system was built. The process of grading included fruit image acquiring, image processing and fruit tracking and separating. In the first section, a new approach of image grabbing by employing an asynchronous reset camera was presented. Three images of the different surfaces of each fruit would be collected by rolling the fruits when they passed through the image-capturing area. To acquire clear images, high-frequency fluorescent lamps supplied by three-phase alternating current were used to illuminate. In the image processing section, the diameter and a color model were used to identify the grade of the fruits. Fruits were graded into four grades by size, and two by color. Each fruit identified was tracked and separated by a novel algorithm which was realized with a PLC (Program Logic Controller). The whole grading system was tested with 1000 citrus. It could work stably when the grading capability was twelve citrus per second and the grading level was nine. The on-line grading results indicated that the accuracy of tracking and separating was higher than 99%, and the ultimate grading error was less than 3%.
机译:研究了基于计算机视觉的水果分级技术,建立了双通道在线自动分级系统。分级过程包括果实图像获取,图像处理和水果跟踪和分离。在第一部分中,提出了采用异步复位相机进行的一种新的图像抓取方法。当通过图像捕获区域时,通过滚动水果来收集每个果实的不同表面的三个图像。为了获得清晰的图像,使用三相交流电流提供的高频荧光灯来照亮。在图像处理部分中,直径和颜色模型用于识别水果的等级。果实按大小分为四个等级,两种颜色分为两种等级。通过使用PLC(程序逻辑控制器)实现的新颖算法跟踪和分离所识别的每个果实。用1000柑橘测试整个分级系统。当分级能力为每秒12个柑橘时,它可以稳定地工作,并且分级水平为九个。在线分级结果表明,跟踪和分离的准确性高于99%,最终分级误差小于3%。

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