基于机器视觉的烧结矿FeO含量在线感知

Online perception on FeO content in sinter based on machine vision

烧结矿FeO的含量是烧结生产的一项综合性指标,影响它的因素较多而且各因素间呈现一种非线性关系,导致对FeO含量的预测难度较大。针对烧结矿FeO含量难以预测的问题,本文提出一种基于机器视觉技术实现烧结矿FeO含量在线感知的系统。该系统通过在烧结台车机尾安装红外热成像设备来获取烧结断面的热成像图片信息并对图片信息特征进行提取和分析,提取的机尾断面图像特征作为Darknet-19算法的输入参数,建立基于改进的Darknet-19算法的烧结矿FeO含量预测模型,实现对烧结矿FeO含量的实时预测。现场实际使用表明,烧结矿FeO含量预测模型的预测值与实际值偏差±0.5时,准确率在82.5%,对稳定和优化烧结生产过程控制有积极作用。

The FeO content of sinter is a comprehensive index of sinter production with many factors affecting it and a nonlinear relationship between the factors,which makes it difficult to predict the FeO content.In order to solve the problem that the FeO content of sinter is difficult to predict,a system based on machine vision technology to realize the online perception of FeO content in sinter is proposed.The system obtains the thermography picture information of the tail section by installing infrared thermography equipment at the tail of the sintering trolley,extracts and analyzes the picture information features,and uses the extracted image features of the tail section as the input parameters of the Darknet-19 algorithm,and establishes a prediction model of sinter FeO content based on the improved Darknet-19 algorithm to realize the real-time prediction of sinter FeO content.The field application shows that when the deviation between the predicted value and the actual value of the sinter FeO content prediction model is±0.5,the accuracy rate is 82.5%,which has a positive effect on stabilizing and optimizing the control of the sintering production process.