LadleVision:Ladle tracking system in Steel Plants
Hot metal, Steel Ladle, and Scrap pot Tracking by auto-capturing the Ladle number and locations at SMS-1 and SMS-2.
Created on 17th April 2024
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LadleVision:Ladle tracking system in Steel Plants
Hot metal, Steel Ladle, and Scrap pot Tracking by auto-capturing the Ladle number and locations at SMS-1 and SMS-2.
The problem LadleVision:Ladle tracking system in Steel Plants solves
Problems with Current Solutions:
- Manual Tracking: Manual data entry of ladle numbers and locations is prone to errors. Typos, illegible handwriting, and misinterpretation can lead to incorrect information being recorded.
- Delayed Updates: Manually updating ladle locations takes time and may not reflect real-time movements accurately. This delay can result in inefficiencies in ladle management & scheduling.
- Limited Range: Hardware solutions like RFID and Bluetooth have limited range capabilities. Ladles that move beyond the tracking range may not be accurately monitored, especially in large industrial facilities.
- Deployment Challenges: Installing hardware devices on ladles, especially in high-temperature and harsh industrial environments, can be challenging and may require specialized equipment and expertise.
Our Solution:
- LadleVision relies on image processing and computer vision to detect and track ladles. Cameras are strategically placed to capture ladle images, and optical character recognition (OCR) identifies ladle numbers.
- A matching algorithm tracks ladles across different camera views, determining their locations. Ladle data, including numbers and positions, is transmitted to a central server in real time.
- This central server performs data analytics, providing insights like circulation times and maintenance predictions, optimizing ladle management.
What makes us different?
- Real-Time Data Transmission: The ability to transmit ladle data, including numbers and positions, to a central server in real time is crucial.
- Data Analytics for Optimization: Our central server performs data analytics, providing valuable insights into circulation times and maintenance predictions.
- User friendly interface: This interface will offer real-time visualizations of ladle movements and status, enabling operators to monitor operations effectively.
Challenges I ran into
One specific challenge we encountered during the development of LadleVision was ensuring accurate ladle tracking across different camera views. This posed a significant hurdle as discrepancies in ladle position detection could lead to incorrect data being transmitted to the central server.
To overcome this challenge, we implemented a robust matching algorithm that reconciled ladle positions from multiple camera perspectives. This algorithm leveraged image processing techniques to identify and track ladles consistently across various views. Additionally, fine-tuning the optical character recognition (OCR) for ladle numbers improved accuracy in identifying and matching ladles.
Our team also conducted extensive testing in simulated and real-world environments to validate the algorithm's performance under different lighting conditions, ladle orientations, and speeds of movement. This iterative testing process helped refine the algorithm and address any edge cases that could affect tracking accuracy.
By combining advanced image processing, OCR refinement, and rigorous testing protocols, we successfully overcame the challenge of accurate ladle tracking, ensuring reliable data transmission and analytics for optimized ladle management.
Tracks Applied (1)
Hardware / IOT
Circuit Central
Technologies used