Abstract
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.
Highlights
In a comminution circuit, it is desirable to have an accurate knowledge of the particle size in various process steps
With the rapid development of artificial intelligence, advanced image processing technology and machine vision have been increasingly utilized in particle size detection
Many researchers have developed particle size detection methods based on traditional image processing techniques and showed significant breakthroughs
Summary
It is desirable to have an accurate knowledge of the particle size in various process steps. Most mineral and crushing plants detect material size by manual screening, which involves enormous human labor resources. With the rapid development of artificial intelligence, advanced image processing technology and machine vision have been increasingly utilized in particle size detection. Image analysis provides the opportunity to gauge the size of rock particles coming out of a crushing process. Many researchers have developed particle size detection methods based on traditional image processing techniques and showed significant breakthroughs. These methods are mainly developed based on watershed-related models [1,2] and threshold segmentation [3,4]
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