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@article{198977,
        author = {V.Maheskumar and S.Srinath and C.Surendhar and R.Venkatesan},
        title = {AI-BASED REAL-TIME RAILWAY ANOMALY AND TRACK DAMAGE DETECTION SYSTEM USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11836-11844},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198977},
        abstract = {Smart Railway Anomaly Detection System is an innovative system developed using artificial intelligence technology. It works in real-time to improve railway operations safety and efficiency. The Smart Railway Anomaly Detection System makes use of new technologies such as computer vision, machine learning, and real-time video analytics for anomaly detection and hazard mitigation along railway tracks. Cameras fitted into locomotives, drones, and rail tracks capture images that are analyzed by powerful deep learning algorithms such as You Only Look Once (YOLO) and Convolutional Neural Networks (CNNs). CNNs use spatial information to recognize the position of different objects such as rubbish, trespassers, wildlife, and railway tools. Also, CNN-based algorithms detect tiny cracks along railway tracks. This proposed system will reduce dependency on human inspection processes since it will provide constant surveillance of the railway network. The system will use a multiple module structure including foreground detection, feature extraction, object detection, object recognition, and track damage detection that will make sure that all anomalies are identified. With this proposed system, it is easy to alert authorities about any issues that may occur, making it easier for them to respond and perform their duty.},
        keywords = {Anomaly Detection, Computer Vision, Deep Learning, Object Detection, Railway Safety, Real-Time Monitoring, YOLO Algorithm.},
        month = {April},
        }