Image Segmentation Method on Quartz Particle-Size Detection by Deep Learning Networks

Abstract

In the beneficiation of quartz sand, hydraulic classification is a primary way to obtain quartz production in various size fractions. It is essential for plants to measure the particle size of quartz sand during the classification, in time to evaluate the classification efficiency. However, the traditional manual-screening method consumes labor and time, while the particle-size analyzer is expensive. Thus, a size-detection method of quartz-sand particle is proposed in this paper, which is based on a deep learning semantic-segmentation network Fully Convolutional Networks (FCN)-ResNet50. The FCN-ResNet50 network sand segments images, and the average particle size of quartz sand is obtained after converting the pixel-particle size to physical-particle size. Using deep learning, the quartz sand with particle sizes of −40 + 70 (0.212–0.38 mm), −70 + 100 (0.15–0.212 mm), −100 + 140 (0.109–0.15 mm), and −140 + 400 (0.038–0.109 mm) meshes, can be measured directly. The results showed that the validation accuracy of the FCN-ResNet50 was over 97%, and the loss value was approximately 0.2. Compared with the UNet-Mobile and Deeplab-Xception, the average error of particle-size detection was approximately 0.01 mm, which was close to the manual calibration-software results. This method has the advantages of quick sampling and low equipment costs, increasing the hydraulic-classification efficiency of quartz sand and promoting automation in the concentrator.