Image Process of Rock Size Distribution Using DexiNed-Based Neural Network
Artikel i vetenskaplig tidskrift, 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

DexiNed

convolutional neural networks

OpenCV

image segmentation

Författare

Haijie Li

Chalmers, Industri- och materialvetenskap, Produktutveckling

FLSmidth AS

Gauti Asbjörnsson

Chalmers, Industri- och materialvetenskap, Produktutveckling

Mats Lindkvist

FLSmidth AS

Minerals

2075-163X (eISSN)

Vol. 11 7 736

Styrkeområden

Produktion

Ämneskategorier

Reglerteknik

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.3390/min11070736

Mer information

Senast uppdaterat

2021-07-14