Published: Jan 10, 2025
enhancing onboard railway inspection with computer vision
A leading railway operator embarked on a transformative project to upgrade its Onboard Railway Inspection System. While the previous system could monitor rail track conditions, it had a high false alarm rate in its defect detection algorithm, leading to inefficient maintenance processes. NCS redesigned the system by integrating advanced AI/ML technologies, driving operational effectiveness and enhancing user experience while ensuring high standards of safety and reliability.
The challenge:
The client faced significant challenges with its existing system, essential for monitoring rail track conditions. The defect detection algorithm produced an unacceptably high false alarm rate, causing unnecessary inspections and straining operational efficiency and resources. This inefficiency threatened the client’s reputation as a dependable railway provider. Addressing these challenges was critical to maintaining high service reliability.
The solution:
NCS redesigned and upgraded the system by continuously improving the AI/ML model using MLOps and fine-tuning techniques. These enhancements reduced false alarms and extended the system’s coverage to additional railway lines, increasing its overall value. The platform’s user interface was also revamped for better accessibility, enabling real-time data insights and more effective data-driven decision-making. These combined improvements boosted preventive maintenance and significantly enhanced the overall user experience.
Snapshot of capabilities:
- Trained Object Detection model achieving >90% accuracy
- Advanced AI/ML Integration with MLOps for continuous model improvement
- Centralised Data Analytics Platform built on the cloud for scalability, seamless integration and data accessibility
- Cloud-based Web Portal designed for improved usability and performance
The impact:
The solution significantly improved preventive maintenance through MLOps and AI/ML model fine-tuning, reducing unnecessary inspections and boosting efficiency. The enhanced user interface and data analytics enabled more data-driven decisions, while expanding coverage to additional railway lines increased service reach. With a model accuracy exceeding 90%, operational costs were reduced and network reliability improved. This further solidified the client’s reputation as a dependable railway provider.