MATLAB-based Railway Safety System

This project uses MATLAB's image processing and deep learning tools to enhance railway safety and prevent track-side accidents. A camera records the view beside the track and an object detection system identifies obstacles such as people or animals. Techniques like edge detection process these issues, while deep learning models like YOLO ensure accurate classification. An alarming system quickly alerts the engine driver, enabling early responses and preventing accidents. The real-time system continuously monitors railway tracks, safeguarding wildlife by reducing train-animal collisions and minimizing operational disruptions. This cost-effective and scalable solution improves railway efficiency while prioritizing safety. By integrating automation, AI, and sensors, it sets a new standard for accident prevention. It also protects endangered species in vulnerable areas and functions efficiently in various environmental conditions. Future enhancements could include remote monitoring and predictive analytics based on the Internet of Things(IoT) to optimize safety, address current challenges, and ensure long-term sustainability for global railway networks.