Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops
A ladle tracking system in steel production plants is essential for optimizing the ladle transportation between different processing units. The currently used technologies for ladle tracking, including Radio Frequency Identification (RFID) systems, are not effective due to their high maintenance cos...
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MDPI AG
2025-06-01
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| Series: | Engineering Proceedings |
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| author | Kabil Murugan Mahinas Senthilmurugan Venbha V. Senthilkumar Harshita Velusamy Karthiga Sekar Vasanthan Buvanesan Manikandan Venugopal |
| author_facet | Kabil Murugan Mahinas Senthilmurugan Venbha V. Senthilkumar Harshita Velusamy Karthiga Sekar Vasanthan Buvanesan Manikandan Venugopal |
| author_sort | Kabil Murugan |
| collection | DOAJ |
| description | A ladle tracking system in steel production plants is essential for optimizing the ladle transportation between different processing units. The currently used technologies for ladle tracking, including Radio Frequency Identification (RFID) systems, are not effective due to their high maintenance costs and poor performance in harsh conditions, leaving a significant gap in developing an automated ladle tracking system. This paper proposes two innovative solutions to address these problems: a computer-vision-based ladle tracking system and an integrated approach of preprocessing techniques with optical character recognition (OCR) algorithms. The first method utilizes a YOLOv8 framework for detecting the two classes from the input images, such as the ladles and their unique numbers. This method achieved a precision of 0.983 and a recall of 0.998 in detecting the classes. The second method involves several preprocessing steps prior to the application of OCR. This is suitable for challenging environments, where the clarity of the images may be compromised. EasyOCR with enhanced preprocessing was able to extract the ladle number with a confidence score of 0.9948. The results demonstrate that vision-based automated ladle tracking is feasible in steel plants, improving operational efficiency, ensuring safety, and minimizing human intervention. |
| format | Article |
| id | doaj-art-fdd2f73516b64a8db0753e286e2f5a8e |
| institution | Matheson Library |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Engineering Proceedings |
| spelling | doaj-art-fdd2f73516b64a8db0753e286e2f5a8e2025-06-25T13:48:21ZengMDPI AGEngineering Proceedings2673-45912025-06-019511110.3390/engproc2025095011Automatic Ladle Tracking with Object Detection and OCR in Steel Melting ShopsKabil Murugan0Mahinas Senthilmurugan1Venbha V. Senthilkumar2Harshita Velusamy3Karthiga Sekar4Vasanthan Buvanesan5Manikandan Venugopal6Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, IndiaA ladle tracking system in steel production plants is essential for optimizing the ladle transportation between different processing units. The currently used technologies for ladle tracking, including Radio Frequency Identification (RFID) systems, are not effective due to their high maintenance costs and poor performance in harsh conditions, leaving a significant gap in developing an automated ladle tracking system. This paper proposes two innovative solutions to address these problems: a computer-vision-based ladle tracking system and an integrated approach of preprocessing techniques with optical character recognition (OCR) algorithms. The first method utilizes a YOLOv8 framework for detecting the two classes from the input images, such as the ladles and their unique numbers. This method achieved a precision of 0.983 and a recall of 0.998 in detecting the classes. The second method involves several preprocessing steps prior to the application of OCR. This is suitable for challenging environments, where the clarity of the images may be compromised. EasyOCR with enhanced preprocessing was able to extract the ladle number with a confidence score of 0.9948. The results demonstrate that vision-based automated ladle tracking is feasible in steel plants, improving operational efficiency, ensuring safety, and minimizing human intervention.https://www.mdpi.com/2673-4591/95/1/11computer visionladleladle tracking systemoptical character recognition (OCR)steel melting shop (SMS) |
| spellingShingle | Kabil Murugan Mahinas Senthilmurugan Venbha V. Senthilkumar Harshita Velusamy Karthiga Sekar Vasanthan Buvanesan Manikandan Venugopal Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops Engineering Proceedings computer vision ladle ladle tracking system optical character recognition (OCR) steel melting shop (SMS) |
| title | Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops |
| title_full | Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops |
| title_fullStr | Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops |
| title_full_unstemmed | Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops |
| title_short | Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops |
| title_sort | automatic ladle tracking with object detection and ocr in steel melting shops |
| topic | computer vision ladle ladle tracking system optical character recognition (OCR) steel melting shop (SMS) |
| url | https://www.mdpi.com/2673-4591/95/1/11 |
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