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@article{191711,
        author = {T. Pandiselvi and R.Reshma and K.M.S. Dhesika and K. Karthiga rani},
        title = {Smart Track Vision: Obstacle Detection in Railways Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8785-8791},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191711},
        abstract = {Due to frequent accidents brought on by unanticipated barriers including people, animals, cars, and natural debris on railway tracks, railway safety is still a major concern. The majority of traditional railway monitoring systems rely on manual inspection and simple sensor-based detection methods, which are frequently unreliable in remote areas and real-time operation. In order to improve railway safety, this study suggests a Smart Track Vision-Based Obstacle Detection System that combines computer vision, deep learning, and Internet of Things (IoT)-based communication technologies.
The suggested solution uses a web camera to record visual information about the railway track environment in real time. The collected frames are processed using a deep learning-based object detection model that was trained with Google Colab and run on a laptop to precisely identify and categorize various obstacle kinds. Vision-based detection is combined with temperature, humidity, and ultrasonic sensors to increase robustness in a variety of environmental situations. To find possible risks, the processing unit combines sensor data with deep learning outputs. When a critical barrier is identified, a GPS module is used to collect specific geographical coordinates, and a GSM module is used to send alert signals to the closest train driver, control center, and railway station. LED and buzzer-based local alerts further guarantee instant on-site awareness. The suggested system offers a scalable and affordable solution for intelligent railway surveillance and accident prevention, with room for future improvement using larger datasets and edge computing platforms. It greatly improves response time and operational safety by offering early warning, precise location tracking, and real-time communication.},
        keywords = {Railway Safety, Obstacle Detection, Deep Learning, Computer Vision, IoT, GPS, GSM, Smart Transportation},
        month = {January},
        }