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Uniform coating thickness is crucial for quality and is achieved using air knives which wipe off excess zinc. At high strip speeds, zinc splatters onto equipment, causing defects and downtime. Parameters such as knife positioning and air pressure influence splatter severity and can be optimised to reduce it. Therefore, this paper proposes a system that converges computer vision and manufacturing whilst addressing some challenges of real-time monitoring in harsh industrial environments, such as the extreme heat, metallic dust, dynamic machinery and high-speed processing at the galvanising site. The approach is primarily comprised of the Counting (CNT) background subtraction algorithm and YOLOv5, which together ensure robustness to noise produced by heat distortion and dust, as well as adaptability to the highly dynamic environment. The YOLOv5 element achieved precision, recall and mean average precision (mAP) values of 1. When validated against operator judgement using mean average error (MAE), interquartile range, median and scatter plot analysis, it was found that there was more discrepancy between the two operators than the operators and the model.This research also strategises the deployment process for integration into the galvanising line. The model proposed allows real-time monitoring and quantification of splatter severity which provides valuable insights into root-cause analysis, process optimisation and maintenance strategies. 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The data used in this study, provided by Tata Steel Europe Ltd., were handled in accordance with data protection and privacy policies to ensure confidentiality and ethical integrity. There are no financial or non-financial conflicts of interest to disclose and the consent of human participants was obtained.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}