Abstract
This paper presents an algorithm for automatic detection of obstructions on railway tracks. Based on computer vision techniques, this algorithm extracts the railway tracks from the image feed and automatically detects obstacles that can endanger normal railway system operation, as well as the safety of its users. To segment the railway tracks, two techniques are explored. First, the Hough transform is used to detect straight lines, which proves to be inefficient when dealing with curves. To overcome this problem, an alternative solution is developed based on mathematical morphology techniques and BLOB (Binary Large OBject) analysis, leading to a more robust segmentation. The surrounding terrain is also subject to analysis. The algorithm’s performance is evaluated considering different scenarios with and without simulated anomalies, demonstrating the effectiveness of the proposed solution.
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Acknowledgements
This research is funded by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2019.
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Kano, G., Andrade, T., Moutinho, A. (2019). Automatic Detection of Obstacles in Railway Tracks Using Monocular Camera. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_26
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DOI: https://doi.org/10.1007/978-3-030-34995-0_26
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