10145409AI-based Stable Hazard Diffusion Framework To Enhance Hazard Detection On Railway Tracks Via Representative Hazard GenerationClosedSmall Business Research InitiativeInnovate UKOur project aims to develop an innovative AI-based hazard detection system tailored for railway networks. The system addresses the critical need for improved safety and operational efficiency by leveraging advanced computer vision techniques and synthetic data generation. The core of our solution involves creating a Stable Hazard Diffusion Framework that generates diverse synthetic hazard scenarios based on real train track footage provided by Petards, enabling more effective training of machine learning models. This approach allows for the detection of multiple and subtle hazards on rail tracks, which traditional systems often struggle to identify due to limited real-world data. Incorporating attention mechanisms, our technology enhances the ability to focus on complex hazards that are difficult to differentiate, ensuring timely and accurate alerts for railway operators. The goal is to reduce maintenance costs and operational disruptions while improving safety across the rail network. We plan to engage with key railway stakeholders throughout the project's three-month timeline, conducting pilot tests to validate our system in real-world conditions. The ultimate aim is to deliver a market-ready product that significantly enhances railway safety and efficiency. By addressing the pressing needs of the railway sector, we hope to contribute to the development of smarter and safer transportation solutions worldwide. This project aligns with the growing demand for advanced monitoring technologies in the global railway safety market, which is projected to expand as regulatory requirements and safety standards evolve. This work will be carried out by the University of Huddersfield, home the Institute of Railway Research which has extensive experience of research and development with a focus on British and international railways. We look forward to sharing our progress and the impact of our work on enhancing railway safety and operational performance.