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An online detection system for aggregate sizes and shapes based on digital image processing

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Abstract

Traditional aggregate size measuring methods are time-consuming, taxing, and do not deliver online measurements. A new online detection system for determining aggregate size and shape based on a digital camera with a charge-coupled device, and subsequent digital image processing, have been developed to overcome these problems. The system captures images of aggregates while falling and flat lying. Using these data, the particle size and shape distribution can be obtained in real time. Here, we calibrate this method using standard globules. Our experiments show that the maximum particle size distribution error was only 3 wt%, while the maximum particle shape distribution error was only 2 wt% for data derived from falling aggregates, having good dispersion. In contrast, the data for flat-lying aggregates had a maximum particle size distribution error of 12 wt%, and a maximum particle shape distribution error of 10 wt%; their accuracy was clearly lower than for falling aggregates. However, they performed well for single-graded aggregates, and did not require a dispersion device. Our system is low-cost and easy to install. It can successfully achieve online detection of aggregate size and shape with good reliability, and it has great potential for aggregate quality assurance.

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Abbreviations

K :

Scaling factor

a :

Particle perimeter

b :

Minor equivalent axis of particle

l c :

Particle perimeter

s :

Particle area

d :

Diameter of aggregate

V :

Particle volume

S :

Sphericity

P equal :

Perimeter of a circle of equal projection area

P real :

Particle projection real perimeter

A:

Projected area

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Acknowledgments

We are indebted to four anonymous reviewers and Chief Editor L. Nasdala for their constructive comments on this paper. This work was financially supported by a special project of the National and International Scientific and Technological Cooperation (2015DFA710402), a major project of the Foundation of Science and Technology in Fujian Province (2016H6013, 2014H6017), a Joint innovation project for Industrial technology in Fujian Province, and a Subsidized Project for Cultivating Postgraduates’ Innovative Ability in Scientific Research at Huaqiao University.

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Authors and Affiliations

  1. College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, China

    Jianhong Yang & Sijia Chen

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  1. Jianhong Yang
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  2. Sijia Chen
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Correspondence to Jianhong Yang.

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Editorial handling: L. Nasdala

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Yang, J., Chen, S. An online detection system for aggregate sizes and shapes based on digital image processing. Miner Petrol 111, 135–144 (2017). https://doi.org/10.1007/s00710-016-0458-y

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