欧亚国际

热点

开发了一个卷积神经网络（CNN）框架，用于通过多光谱成像识别煤和煤矸石。

比较不同波长下多光谱成像的识别效果。

基于贝叶斯优化算法优化CNN模型的超参数。

CNN模型对噪声信号具有一定的抗干扰能力。

摘要

煤矸石的精确分类是实现有效分选和高效利用的关键环节。然而，传统方法存在着耗水量大、煤泥污染大、环境因素影响大等缺点。本文将多光谱成像技术与卷积神经网络（CNN）相结合，对煤矸石进行分类，利用贝叶斯算法对CNN模型的超参数进行优化。采集了淮南矿区209块煤和201块煤矸石在675-975nm范围内的多光谱图像。采用CNN和传统的建模方法（图像特征提取和分类器相结合的策略）建立识别模型，并在煤矸石多光谱数据集上对分类结果进行了分析和比较。基于CNN的识别分析模型表现最好，F1得分达到1.00。此时，模型的超参数如下：网络深度为1，初始学习率为0.012939，随机梯度下降动量为0.83813，L2正则化强度为0.0099852。此外，通过引入不同级别的噪声信号，验证了CNN识别模型的鲁棒性。基于CNN的识别分析模型能够快速、准确地识别煤矸石，无需复杂的图像处理步骤，具有一定的抗干扰能力，将推动煤矸石自动分选技术的进步。

关键词：多光谱成像，卷积神经网络，煤矸石鉴定，贝叶斯优化算法 

A Bayesian optimal convolutional neural network approach for classification of coal and gangue with multispectral imaging

Highlights 

Develop a convolutional neural network (CNN) frame for identifying coal and gangue by multispectral imaging.

Compare the recognition effects of multispectral imaging at different wavelengths.

Optimize hyperparameters of CNN model based on Bayesian optimization algorithm.

The CNN model has certain anti-interference ability to noise signal.

Abstract

The precise classification of coal and gangue is a crucial link for effective sorting and efficient utilization. However, there are some shortcomings in traditional methods, such as water consumption, coal slime pollution, and great influence of environmental factors, and so on. Here, multispectral imaging technology combined with the convolutional neural network (CNN) was applied to classify coal and gangue, in which the hyperparameters of the CNN model were optimized by Bayesian algorithm. The multispectral images in the range of 675–975 nm of 209 pieces of coal and 201 pieces of gangue, which came from the Huainan mining area, were collected. The CNN and traditional modeling methods (combination strategy of image feature extraction and classifier) were employed to develop identification models, and the classification results were analyzed and compared on the multispectral dataset of coal and gangue. The identification analysis model based on CNN had the best performance, and the F1 score reached 1.00. At this time, the hyperparameters of the model are as follows: network depth was 1, initial learning rate was 0.012939, random gradient descent momentum was 0.83813, and L2 regularization intensity was 0.0099852. Moreover, the robustness of the CNN identification model was verified by introducing different levels of noise signals. The identification analysis model based on the CNN can quickly and accurately identify coal and gangue without complex image processing steps, and the model has certain anti-interference ability, which will promote the progress of automatic separation technology for coal and gangue.

Keywords

Multispectral imaging

Convolutional neural network

Coal-gangue identification

Bayesian optimization algorithm