Image Process of Rock Size Distribution Using DexiNed-Based Neural Network
Journal article, 2021

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

image processing

particle size distribution


convolutional neural networks


image segmentation


Haijie Li

Chalmers, Industrial and Materials Science, Product Development

FLSmidth AS

Gauti Asbjörnsson

Chalmers, Industrial and Materials Science, Product Development

Mats Lindkvist

FLSmidth AS


2075-163X (eISSN)

Vol. 11 7 736

Areas of Advance


Subject Categories

Control Engineering

Signal Processing

Computer Vision and Robotics (Autonomous Systems)



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