Railway transport is the most cost-effective and best mode of passenger transportation in India, as well as for long-distance and suburban traffic. Railway track crossings and unseen cracks in railway tracks are the leading causes of railway accidents. Railway track inspection and monitoring are currently performed manually, which is time-consuming and inaccurate due to the high possibility of human error. Railway infrastructure plays a vital role in transportation, and ensuring its safety is paramount. This abstract introduces an innovative system designed to enhance the monitoring and safety of railway tracks by integrating crack detection technology with animal disturbance alerts. The proposed system utilizes advanced sensors and image processing techniques to detect cracks and structural abnormalities on railway tracks. In addition to crack detection, the system incorporates animal disturbance alert functionality. Wildlife incursions onto railway tracks pose a significant safety risk, not only to the animals but also to train operations. The system employs infrared sensors and machine learning algorithms to identify and alert railway authorities about the presence of animals on or near the tracks, allowing for timely intervention to prevent potential collisions. The integration of crack detection and animal disturbance alert features into a unified system provides a holistic approach to railway track safety. Real-time data from the system can be transmitted to a central monitoring station, enabling prompt response to any identified issues. This innovation aims to minimize the risk of accidents, improve overall railway safety, and contribute to the efficient and reliable operation of rail networks.


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    Title :

    Crack Detection on Railway Tracks with Animal Disturbance Alert


    Additional title:

    Algorithms for Intelligent Systems



    Conference:

    International Conference on Innovations in Cybersecurity and Data Science Proceedings of ICICDS ; 2024 ; Bengaluru, India March 15, 2024 - March 16, 2024



    Publication date :

    2024-12-13


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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