电子测量与仪器学报

孙文强,刘辉.基于改进完全局部二值模式火焰特征提取的转炉炼钢终点碳含量预测[J].电子测量与仪器学报,2021,35(10):56-64

基于改进完全局部二值模式火焰特征提取的转炉炼钢终点碳含量预测

Carbon content prediction of converter steelmaking based onimproved CLBP flame feature extraction

DOI：

英文关键词:completely local binary mode converter steelmaking end carbon local phase quantization color weighting

基金项目:国家自然科学基金（61863018）、云南省科技厅（202001AT070038）项目资助

作者	单位
孙文强	1.昆明理工大学信息工程与自动化学院
刘辉	1.昆明理工大学信息工程与自动化学院

Author	Institution
Sun Wenqiang	1.School of Information Engineering and Automation, Kunming University of Science and Technology
Liu Hui	1.School of Information Engineering and Automation, Kunming University of Science and Technology

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中文摘要:

转炉炼钢火焰图像特征的准确提取是预测终点碳含量的关键,针对于火焰图像相似性高进而难以区分碳含量相近的火焰图像,导致无法准确精准预测碳含量的问题,提出一种改进完全局部二值模式(improved complete local binary pattern, ICLBP) 的彩色纹理特征提取方法,用于提取不同碳含量下更具区分性的炉口火焰图像特征并进行终点碳含量的预测。首先,在不同颜色通道下采用局部相位量化(local phase quantization, LPQ)提取图像相位信息,与 CLBP 提取的图像幅值信息组合成融合特征 ICLBP_MP,以增强 CLBP 算法结构的鲁棒性;然后,通过改进的颜色信息加权策略对其进行加权,以增强火焰图像的颜色对比度信息;最后,使用 K 近邻回归模型对碳含量进行预测。实验结果表明,碳含量预测在 0. 02%误差范围内的准确率为 83. 9%。

英文摘要:

Accurate extraction of flame image features for converter steelmaking is the key to predicting end point carbon content. Aiming at the high similarity of flame images, it is difficult to distinguish flame images with similar carbon content, which leads to the problem that the carbon content cannot be accurately predicted. In this paper, an improved complete local binary pattern (ICLBP) color texture feature extraction method is proposed to extract more differentiated flame features at furnace mouth under different carbon contents and predict the endpoint carbon content. Firstly, local phase quantization ( LPQ) is used to extract image frequency domain phase information under different color channels, and the fusion feature ICLBP _ MP is combined with image spatial domain amplitude information extracted by CLBP to enhance the robustness of CLBP algorithm structure. Then, it is weighted by an improved color information weighting strategy to enhance the color contrast information of the flame image. Finally, the K nearest neighbor regression model is used to predict the carbon content. The experimental results show that the accuracy rate of carbon content prediction is 83. 9% within the error range of 0. 02%.

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