基于形态重建和反向跟踪的粗集料级配视觉检测

doi:10.3969/j.issn.1000-1158.2021.06.04

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摘要利用图像处理方法对下料过程中的粗集料图像进行了级配检测研究。采用基于掩膜的ROI提取方法,结合形态重建手段,剔除了图像上下边界处的不完整集料颗粒;针对连续图像中集料颗粒重复出现的问题,对图像进行间隔采样,并提出反向跟踪算法,对集料颗粒进行跟踪识别,避免同一集料颗粒在连续图像中被重复统计;采用等效椭圆Feret短径作为等效粒径进行集料粒径分析,并对像素尺寸和实际尺寸的转换关系进行优化,得到集料级配结果。实验结果表明,反向跟踪算法对集料颗粒的识别率为98.08%,集料级配结果的准确率为95.59%。

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	陈泽琦
	范伟军
	郭斌
	江文松

关键词 ：计量学;视觉检测;粗集料级配;形态重建;反向跟踪;ROI提取方法, 粒径分布

Abstract：Based on image process method, the detection of particle size for the moving coarse aggregate during the blanking process was studied. A mask-based region of interest (ROI) extraction method combined with the region growth method was introduced to solve the statistical problem of incomplete aggregates at the edge of the image. And a ROI image was gotten in which the aggregate particles had a complete morphology. Then, a statistical method determined the maximum frame sampling interval, ensuring every aggregate counted and image processing efficiency increased. Aiming at solving the problem of aggregates which appeared repeatedly in consecutive images, a reverse tracking algorithm based on touch-type association gate was proposed to recognize the recurring aggregate particles, which avoided the repeated statistics of the same aggregate particles in consecutive images. Finally, the aggregate size distribution was analyzed by using the equivalent ellipse Feret minor axis as the equivalent particle size of aggregate and optimizing the relationship between pixel size and actual size. The experimental results show that the accuracy of recurring particle recognition is 98.08% and the accuracy of aggregate size distribution is 95.59%.

Key words： metrology visual detection grading of coarse aggregate morphological reconstruction reverse tracking ROI extraction method particle size distribution

收稿日期: 2020-06-19 发布日期: 2021-06-23

PACS:

TB96

基金资助:工信部2018年智能制造新模式应用项目(Z135060009002);浙江省重大科技专项(2018C01063);浙江省自然科学基金(LQ20E050016);浙江省新苗人才项目(2020R409053)

通讯作者: 范伟军(1973-),湖南邵阳人, 中国计量大学副教授, 硕士生导师,研究方向为图像处理与模式识别及工业自动化检测。Email: 864841569@qq.com E-mail: 864841569@qq.com

作者简介: 陈泽琦(1996-),浙江慈溪人,中国计量大学硕士研究生,研究方向为图像处理与模式识别。Email: z1802085202@cjlu.edu.cn

引用本文:

陈泽琦,范伟军,郭斌,江文松. 基于形态重建和反向跟踪的粗集料级配视觉检测[J]. 计量学报, 2021, 42(6): 710-717.
CHEN Ze-qi,FAN Wei-jun,GUO Bin,JIANG Wen-song. A Visual Detection Method for Particle Size of Moving Coarse Aggregate Based on Morphological Reconstruction and Reverse Tracking. Acta Metrologica Sinica, 2021, 42(6): 710-717.

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