Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation
Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation
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DOI：
10.1007/s00170-023-11151-4
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发表时间：
2023-03
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影响因子：
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通讯作者：
Callum O'Donovan;Ivan Popov;Grazia Todeschini;C. Giannetti
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作者：
Callum O'Donovan;Ivan Popov;Grazia Todeschini;C. Giannetti
由于物联网(IoT)和大数据的兴起而兴起的数据收集，计算机视觉中的深度学习在许多应用领域中正变得越来越流行和有用，用于跟踪对象的运动。到目前为止，计算机视觉在工业中主要用于质量检测目的，如表面缺陷检测；然而，一个新兴的研究领域是涉及实时跟踪移动机械的过程监控应用。在炼钢中，恶劣的环境、恶劣的照明条件和烟雾的存在阻碍了计算机视觉在过程监控中的应用。因此，计算机视觉的应用还没有得到深入的研究。提出了一种弱光照条件下铁水浇注过程跟踪的新方法。该方法使用对比度受限的自适应直方图均衡化(CLAHE)进行对比度增强，使用MASK R-CNN进行分割预测，并使用卡尔曼滤波来改进预测。像素级跟踪使浇注高度和旋转角估计成为可控参数。火焰的严重程度也被估计为表明工艺质量。用钢包浇注的实际数据对该方法进行了验证。目前，还没有出版物提供一种追踪钢包浇注的方法。根据微软通用上下文对象(MSCOCO)标准，该模型的平均平均精度(MAP)为0.61。它测量具有高度可变性的过程中的关键过程参数和过程质量，通过根本原因分析、过程优化和预测性维护为过程改进做出重大贡献。通过实时跟踪，预测可以使钢包控制自动化，实现闭环控制，将排放降至最低，并消除人为错误带来的可变性。
Deep learning in computer vision is becoming increasingly popular and useful for tracking object movement in many application areas, due to data collection burgeoning from the rise of the Internet of Things (IoT) and Big Data. So far, computer vision has been used in industry predominantly for quality inspection purposes such as surface defect detection; however, an emergent research area is the application for process monitoring involving tracking moving machinery in real time. In steelmaking, the deployment of computer vision for process monitoring is hindered by harsh environments, poor lighting conditions and fume presence. Therefore, application of computer vision remains unplumbed. This paper proposes a novel method for tracking hot metal ladles during pouring in poor lighting. The proposed method uses contrast-limited adaptive histogram equalisation (CLAHE) for contrast enhancement, Mask R-CNN for segmentation prediction and Kalman filters for improving predictions. Pixel-level tracking enables pouring height and rotation angle estimation which are controllable parameters. Flame severity is also estimated to indicate process quality. The method has been validated with real data collected from ladle pours. Currently, no publications presenting a method for tracking ladle pours exist. The model achieved a mean average precision (mAP) of 0.61 by the Microsoft Common Objects in Context (MSCOCO) standard. It measures key process parameters and process quality in processes with high variability, which significantly contributes to process enhancement through root-cause analysis, process optimisation and predictive maintenance. With real-time tracking, predictions could automate ladle controls for closed-loop control to minimise emissions and eliminate variability from human error.