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Article

Image Process of Rock Size Distribution Using DexiNed-Based Neural Network

1
Mining R&D, FLSmidth A/S, 2500 Valby, Denmark
2
Department of Industrial and Materials Science, Chalmers University of Technology, 412 96 Gothenburg, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Lidia Auret and Kevin Brooks
Minerals 2021, 11(7), 736; https://doi.org/10.3390/min11070736
Received: 31 May 2021 / Revised: 2 July 2021 / Accepted: 2 July 2021 / Published: 7 July 2021
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
In an aggregate crushing plant, the crusher performances will be affected by the variation from the incoming feed size distribution. Collecting accurate measurements of the size distribution on the conveyors can help both operators and control systems to make the right decisions in order to reduce overall power consumption and avoid undesirable operating conditions. In this work, a particle size distribution estimation method based on a DexiNed edge detection network, followed by the application of contour optimization, is proposed. The proposed framework was carried out in the four main steps. The first step, after image preprocessing, was to utilize a modified DexiNed convolutional neural network to predict the edge map of the rock image. Next, morphological transformation and watershed transformation from the OpenCV library were applied. Then, in the last step, the mass distribution was estimated from the pixel contour area. The accuracy and efficiency of the DexiNed method were demonstrated by comparing it with the ground-truth segmentation. The PSD estimation was validated with the laboratory screened rock samples. View Full-Text
Keywords: image processing; image segmentation; particle size distribution; OpenCV; convolutional neural networks; DexiNed image processing; image segmentation; particle size distribution; OpenCV; convolutional neural networks; DexiNed
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MDPI and ACS Style

Li, H.; Asbjörnsson, G.; Lindqvist, M. Image Process of Rock Size Distribution Using DexiNed-Based Neural Network. Minerals 2021, 11, 736. https://doi.org/10.3390/min11070736

AMA Style

Li H, Asbjörnsson G, Lindqvist M. Image Process of Rock Size Distribution Using DexiNed-Based Neural Network. Minerals. 2021; 11(7):736. https://doi.org/10.3390/min11070736

Chicago/Turabian Style

Li, Haijie, Gauti Asbjörnsson, and Mats Lindqvist. 2021. "Image Process of Rock Size Distribution Using DexiNed-Based Neural Network" Minerals 11, no. 7: 736. https://doi.org/10.3390/min11070736

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