Abstract
This paper presents a vision-based ladle monitoring system for steel factories, consisting of two modules: one for ladle surface temperature analysis using thermal cameras and another for deep learning-based detection and recognition of ladle identification numbers. The first module monitors ladle thermal behavior by capturing high-resolution thermal images and employing advanced image analysis techniques. This enhances safety and efficiency in steel production. The second module focuses on digit recognition on the ladle surface, providing crucial identification and tracking information. A robust deep learning model trained on a large dataset of thermal camera images achieves high accuracy in ladle identification. The proposed system integrates thermal cameras and advanced image analysis techniques, offering real-time monitoring, early anomaly detection, and accurate ladle identification. Experimental evaluations demonstrate its effectiveness, indicating its potential for practical implementation in steel factory environments.
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Acknowledgment
This work has been funded by the EU project HyperCOG (Grant agreement number 869886).
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Selim, M. et al. (2024). Vision-Based Ladle Monitoring System for Steel Factories. In: Wagner, A., Alexopoulos, K., Makris, S. (eds) Advances in Artificial Intelligence in Manufacturing. ESAIM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-57496-2_19
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DOI: https://doi.org/10.1007/978-3-031-57496-2_19
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