Abstract
The emergence of low-cost commercial drones fitted with a camera are ideal platforms for remotely monitoring critical assets such as railway corridor. The proposed system employs drones to automate and make the process efficient. In this paper, a railway monitoring system capable of detection and classification of various railway-related infrastructures such as lines, ballast, anchors, sleepers and fasteners using visual images captured by a drone is proposed. The first stage of classification uses a deep network that helps in qualifying the presence of track in a given frame. The second stage helps in classification of objects within a frame for further analysis. Two different deep architectures are used in classification of railway infrastructure—the first for offline analysis that uses transfer learning using a pre-trained GoogLeNet model and the second approach that uses a new architecture for embedded implementation. Transfer learning results in an overall f-score of 89%, and the new architecture results in an overall f-score of 81% with at least 10\(\times \) reduction in parameters.
S. Ikshwaku and A. Srinivasan were affiliated to BMS College of Engineering when the reported work was undertaken. Both authors contributed equally to this work.
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Ikshwaku, S., Srinivasan, A., Varghese, A., Gubbi, J. (2019). Railway Corridor Monitoring Using Deep Drone Vision. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_28
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DOI: https://doi.org/10.1007/978-981-13-1135-2_28
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