论文标题
基于视觉的缺陷分类和水稻核的重量估计
Vision-Based Defect Classification and Weight Estimation of Rice Kernels
论文作者
论文摘要
大米是世界许多地区的主要主食之一。大米内核的质量估计在食品安全和社会经济影响方面至关重要。这通常是由质量检查员过去进行的，这可能会导致客观和主观的不准确性。在本文中，我们提出了一个自动视觉质量估计系统的水稻内核，根据它们的缺陷类型对采样的水稻内核进行分类，并通过透视核类型的重量比评估其质量。为了补偿不同内核数的不平衡并准确地用多个缺陷对内核进行分类，我们提出了一个多阶段的工作流程，该工作流能够在捕获的图像中定位内核并对其属性进行分类。我们定义了一个新型度量，以测量图像中每个内核的相对重量，从而使每种类型的内核相对重量相对于所有样品的相对权重可以计算并用作水稻质量估计的基础。进行了各种实验，以表明我们的系统能够以非接触式的方式输出精确的结果，并取代乏味和容易出错的手动工作。
Rice is one of the main staple food in many areas of the world. The quality estimation of rice kernels are crucial in terms of both food safety and socio-economic impact. This was usually carried out by quality inspectors in the past, which may result in both objective and subjective inaccuracies. In this paper, we present an automatic visual quality estimation system of rice kernels, to classify the sampled rice kernels according to their types of flaws, and evaluate their quality via the weight ratios of the perspective kernel types. To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties. We define a novel metric to measure the relative weight of each kernel in the image from its area, such that the relative weight of each type of kernels with regard to the all samples can be computed and used as the basis for rice quality estimation. Various experiments are carried out to show that our system is able to output precise results in a contactless way and replace tedious and error-prone manual works.
