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 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.

Automated summary

The study introduces a method for particle size distribution estimation based on the DexiNed edge detection network, followed by contour optimization, executed in four main steps including utilizing a modified DexiNed convolutional neural network for edge map prediction, applying morphological and watershed transformations, and estimating mass distribution from pixel contour area. The accuracy and efficiency of the DexiNed method were validated by comparison with ground-truth segmentation and laboratory screened rock samples.