基于图像识别的高纯度云母矿分选研究

王紫越, 张翼, 李星辉, 李俊, 牟少樊, 李佳楠. 基于图像识别的高纯度云母矿分选研究[J]. 矿产综合利用, 2024, 45(3): 81-85. doi: 10.3969/j.issn.1000-6532.2024.03.013
引用本文: 王紫越, 张翼, 李星辉, 李俊, 牟少樊, 李佳楠. 基于图像识别的高纯度云母矿分选研究[J]. 矿产综合利用, 2024, 45(3): 81-85. doi: 10.3969/j.issn.1000-6532.2024.03.013
WANG Ziyue, ZHANG Yi, LI Xinghui, LI Jun, MOU Shaofan, LI Jianan. Study on Separation of High Purity Mica Ore Based on Image Recognition[J]. Multipurpose Utilization of Mineral Resources, 2024, 45(3): 81-85. doi: 10.3969/j.issn.1000-6532.2024.03.013
Citation: WANG Ziyue, ZHANG Yi, LI Xinghui, LI Jun, MOU Shaofan, LI Jianan. Study on Separation of High Purity Mica Ore Based on Image Recognition[J]. Multipurpose Utilization of Mineral Resources, 2024, 45(3): 81-85. doi: 10.3969/j.issn.1000-6532.2024.03.013

基于图像识别的高纯度云母矿分选研究

详细信息
    作者简介: 王紫越(1997-),女,研究生,主要从事选矿自动化方面的研究工作
    通讯作者: 张翼(1982-),男,博士,教授,主要从事选矿自动化方面的教学与研究工作
  • 中图分类号: TD97

Study on Separation of High Purity Mica Ore Based on Image Recognition

More Information
  • 这是一篇工艺矿物学领域的论文。为了解决高纯度云母中色斑杂质分选难的问题,本文提出一种基于图像处理的含杂质云母的分选方法,针对云母在不同光源条件下色斑杂质所呈现的状态,研究了图像光照环境的影响,并在此基础上对所获取图像进行高斯滤波、canny边缘检测等处理方法的研究,最终准确地实现云母矿的图像识别与区分,并在三种不同种类的云母矿识别中取得较好的效果。

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  • 图 1  光照方式与光照条件对比

    Figure 1. 

    图 2  灰度图像

    Figure 2. 

    图 3  滤波图像

    Figure 3. 

    图 4  云母片轮廓

    Figure 4. 

    图 5  云母区域的二值化图像

    Figure 5. 

    图 6  结果图像

    Figure 6. 

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出版历程
收稿日期:  2022-08-26
刊出日期:  2024-06-25

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