浮选过程中的一种基于改进的 U-Net++ 语义分割的泡沫速度测量方法

陈一玮; 徐德刚; 万坤

doi:10.1007/s12613-023-2787-2

Volume 31 Issue 8

Aug. 2024

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 31(8): 1816-1827

Yiwei Chen, Degang Xu, and Kun Wan, A froth velocity measurement method based on improved U-Net++ semantic segmentation in flotation process, Int. J. Miner. Metall. Mater., 31(2024), No. 8, pp. 1816-1827. https://doi.org/10.1007/s12613-023-2787-2

Cite this article as:

Yiwei Chen, Degang Xu, and Kun Wan, A froth velocity measurement method based on improved U-Net++ semantic segmentation in flotation process, Int. J. Miner. Metall. Mater., 31(2024), No. 8, pp. 1816-1827. https://doi.org/10.1007/s12613-023-2787-2

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研究论文

浮选过程中的一种基于改进的 U-Net++ 语义分割的泡沫速度测量方法

中南大学自动化学院, 长沙 410083, 中国

通讯作者:
徐德刚 E-mail: dgxu@csu.edu.cn

文章亮点

(1) 手动构建了钨精矿浮选图像数据集。

(2) 利用形变场对图片及标签自适应地生成变换，以达到数据增强的目的。

(3) 提出了一种适用于浮选泡沫图像的改进分割网络，提升了分割结果的精度和准确性。

(4) 利用语义分割的结果，实现了泡沫实时流速的提取。

中文摘要

在浮选过程中，泡沫图像的特征参数和精矿品位和对应工况高度相关。在不同的工况下，气泡的颜色、大小等静态特征和速度等动态特征有着明显的区别。这些特征的提取通常依赖于泡沫边缘的图像分割结果，因此泡沫图像的分割是研究其视觉信息的基础。同时，由于缺乏科学可靠的带标签的训练数据，且必须手动构建数据集和标签，这给矿物浮选研究带来了困难。为解决这一问题，本文构建了钨精矿浮选图像数据集，并提出了基于cGAN 的数据增强网络和基于 U-Net++ 的泡沫图像分割网络。本文测试了该算法的性能，并与其他算法进行了比较。最后，还基于语义分割的结果，提出了基于相位相关的泡沫流速特征检测方法。

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Research Article

A froth velocity measurement method based on improved U-Net++ semantic segmentation in flotation process

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Abstract

Abstract

During flotation, the features of the froth image are highly correlated with the concentrate grade and the corresponding working conditions. The static features such as color and size of the bubbles and the dynamic features such as velocity have obvious differences between different working conditions. The extraction of these features is typically relied on the outcomes of image segmentation at the froth edge, making the segmentation of froth image the basis for studying its visual information. Meanwhile, the absence of scientifically reliable training data with label and the necessity to manually construct dataset and label make the study difficult in the mineral flotation. To solve this problem, this paper constructs a tungsten concentrate froth image dataset, and proposes a data augmentation network based on Conditional Generative Adversarial Nets (cGAN) and a U-Net++-based edge segmentation network. The performance of this algorithm is also evaluated and contrasted with other algorithms in this paper. On the results of semantic segmentation, a phase-correlation-based velocity extraction method is finally suggested.
- froth flotation;
- froth segmentation;
- froth image;
- data augmentation;
- velocity extraction;
- image features

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References

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