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Improving Blast Furnace Raceway Blockage Detection. Part 3: Visual Detection Based on Tuyere Camera Images

机译:改进高炉滚道堵塞检测。第3部分:基于风口照相机图像的视觉检测

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The present paper is part 3 of a paper series discussing raceway blockages and various approaches for automated detection based on blast furnace (BF) plant data. While part 1~(1)) gave an overview on the different appearances of raceway blockages and part 2~(2)) focussed on signal processing of hot blast data, this third part discusses various approaches for image processing of tuyere camera data. The visual impression of raceway blockages strongly varies between different events. This makes automated detection based on digital image processing of tuyere cameras a difficult task. On one hand the image processing algorithm should be robust and easy to tune for different tuyeres or different blast furnaces, on the other hand it should be fast enough, so that all tuyeres of a blast furnace can be processed on-line with a sufficiently high image frame rate. While algorithms optimized for motion detection fail due to the lack of a homogeneous background, adaptive thresholding of the grey-level histograms delivers useful results. Due to the nature of chaotic motion of coke particles inside the raceway also line based processing methods can extract the information from tuyere images in a sufficient manner and are very fast with regards to online implementation in a process control system. However, image processing of tuyere camera data has some disadvantages compared to the signal processing of hot blast data as discussed in part 2 of this paper.
机译:本文是论文系列的第3部分,讨论了滚道阻塞和基于高炉(BF)工厂数据的各种自动检测方法。尽管第1〜(1))部分概述了滚道阻塞的不同外观,而第2〜(2))部分着重讨论了热风数据的信号处理,但第3部分讨论了风口照相机数据的图像处理的各种方法。滚道堵塞的视觉印象在不同事件之间差异很大。这使得基于风口照相机的数字图像处理的自动检测成为一项艰巨的任务。一方面,图像处理算法应健壮且易于针对不同的风口或不同的高炉进行调整;另一方面,图像处理算法应足够快,以使高炉的所有风口都可以在线进行足够高的处理。图像帧速率。尽管针对运动检测而优化的算法由于缺少均一的背景而失败,但灰度直方图的自适应阈值提供了有用的结果。由于滚道内焦炭颗粒的混沌运动的性质,基于线的处理方法也可以以足够的方式从风口图像中提取信息,并且在过程控制系统中的在线实现方面非常快。但是,与本文第二部分讨论的热风数据的信号处理相比,风口照相机数据的图像处理具有一些缺点。

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