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A real-time mathematical computer method for potato inspection using machine vision

机译:基于机器视觉的马铃薯实时检测数学方法

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摘要

Detection of external defects on potatoes is the most important technology in the realization of automatic potato sorting stations. This paper presents a hierarchical grading method applied to the potatoes. In this work a potato defect detection combining with size sorting system using the machine vision will be proposed. This work also will focus on the mathematics methods used in automation with a particular emphasis on the issues associated with designing, implementing and using classification algorithms to solve equations. In the first step, a simple size sorting based on mathematical binarization is described, and the second step is to segment the defects; to do this, color based classifiers are used. All the detection standards for this work are referenced from the United States Agriculture Department, and Canadian Food Industries. Results show that we have a high accuracy in both size sorting and classification. Experimental results show that support vector machines have very high accuracy and speed between classifiers for defect detection.
机译:在马铃薯自动分拣站的实现中,检测马铃薯的外部缺陷是最重要的技术。本文提出了一种适用于马铃薯的分级分级方法。在这项工作中,将提出一种结合了使用机器视觉的马铃薯缺陷检测和尺寸分类系统的方案。这项工作还将重点关注自动化中使用的数学方法,特别着重于与设计,实现和使用分类算法求解方程式相关的问题。第一步,描述了基于数学二值化的简单尺寸分类,第二步是分割缺陷。为此,使用基于颜色的分类器。美国农业部和加拿大食品工业公司引用了这项工作的所有检测标准。结果表明,我们在大小排序和分类方面都具有很高的准确性。实验结果表明,支持向量机在分类器之间的缺陷检测具有很高的准确性和速度。

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