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

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 o...

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Bibliographic Details
Published in:Minerals (Basel) 2021-07, Vol.11 (7), p.736
Main Authors: Li, Haijie, Asbjörnsson, Gauti, Lindqvist, Mats
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Contours
Control systems
convolutional neural networks
DexiNed
Edge detection
Genetic transformation
image processing
Image segmentation
Mass distribution
Methods
Morphology
Neural networks
OpenCV
Optimization
Particle size
Particle size distribution
Power consumption
Rocks
Sediment samples
Size distribution
Watersheds
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Summary: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.
ISSN:2075-163X
2075-163X
DOI:10.3390/min11070736