Enhancing Railway Safety is Leveraging Local and Global Information for Obstacle Detection

Enhancing railway safety by leveraging local and global information for obstacle detection using OpenCV plays a vital role in preventing accidents and ensuring efficient railway operations. Our approach utilizes computer vision techniques and real-time image processing to detect obstacles on railway tracks with high accuracy. After performing the experiment, we achieved improved detection accuracy, faster processing time, and minimized false alarms, making the system more reliable for real-world applications. This innovative approach integrates OpenCV with AI-driven predictive analysis and cloud-based monitoring, offering a scalable and cost-effective solution compared to conventional obstacle detection methods. Railway safety is a critical concern for transportation networks worldwide. With increasing rail traffic and growing concerns about accidents caused by obstacles on tracks, efficient detection and mitigation strategies are essential. This research paper explores the integration of local and global information for obstacle detection, leveraging advanced technologies such as artificial intelligence (AI), and geospatial data analytics. By combining real-time local sensor inputs with global datasets, railway safety can be significantly enhanced, reducing the risk of accidents and improving operational efficiency. The paper further discusses methodologies, case studies, results, and future work to create a comprehensive safety framework.