| 基于机器视觉的石榴品质自动分级方法 |
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| 引用本文： | 罗山,侯俊涛,郑彬.基于机器视觉的石榴品质自动分级方法[J].中国农机化学报,2023,44(3):117. |
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| 作者姓名： | 罗山 侯俊涛 郑彬 |
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| 作者单位： | 1. 攀枝花学院电气信息工程学院，四川攀枝花，617000；
2. 攀枝花学院智能制造学院，四川攀枝花，617000
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| 基金项目： | 攀枝花市指导性科技计划项目(2019ZD—N—2) |
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| 摘 要： | 采用人工检测的石榴外观品质等级分级方法存在准确率和效率低的问题，提出一种基于机器视觉的石榴品质分级方法。首先，采用机器视觉系统采集石榴样本图像，进行去噪处理与获取掩模图像；其次，提取去噪图像的红、绿、蓝分量，用蓝色分量减去红、绿色分量得到色差图像，并对色差图像进行阈值分割；然后，对分割图像采用数学形态学处理获得连通的疑似缺陷区域的边界，提取纹理特征并根据缺陷与非缺陷区域纹理特征的不同来标记缺陷区域；最后，将缺陷面积与总面积之比和缺陷数目作为划分等级的依据，对石榴品质等级进行划分。试验结果表明：本方法总体分级准确率达到92.9%，能够高效、准确地识别石榴表面缺陷并进行品质分级，为实现自动分级的产业化提供思路。
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| 关 键 词： | 机器视觉 石榴 品质分级 表面缺陷 色差分量 |
Automatic grading method of pomegranate quality based on machine vision |
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| Abstract: | The method of grading pomegranate appearance quality by manual inspection has low accuracy and efficiency. A pomegranate quality classification method based on machine vision is proposed. Firstly, the pomegranate sample image is collected by machine vision system, denoised and the mask image is obtained. Secondly, the red, green and blue components of the denoised image are extracted, the red and green components are subtracted from the blue component to obtain the color difference image, and the color difference image is segmented by threshold. Then, the boundary of the connected suspected defect region is obtained by mathematical morphology processing, the texture features are extracted, and the defect region is marked according to the different texture features of the defect and non defect region. Finally, the ratio of defect area to total area and the number of defects are used as the basis for grading, and the quality grade of pomegranate is divided. The experimental results show that the overall classification accuracy of this method is 92.9%, which can effectively and accurately identify the surface defects of pomegranate and classify the quality, and which provides an idea for the industrialization of automatic classification. |
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| Keywords: | machine vision pomegranate quality grading surface defect color difference component |
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