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Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation
The International Journal of Advanced Manufacturing Technology, Volume: 126, Issue: 3-4, Pages: 1397 - 1416
Swansea University Authors: CALLUM O'DONOVAN, IVAN POPOV, Grazia Todeschini, Cinzia Giannetti
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DOI (Published version): 10.1007/s00170-023-11151-4
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 quali...
|Published in:||The International Journal of Advanced Manufacturing Technology|
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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.
Computer vision, Deep learning, Manufacturing, Remote condition monitoring, Segmentation
Faculty of Science and Engineering
EPSRC; European Social Fund; European Regional Development Fund