Abstract
In this paper we present a visual based approach, utilized for the detection of concrete defects in tunnels. The detection mechanism is a hybrid approach, based on both image processing and deep learning models. Initial detections are validated by an expert, in order to create a robust data set in short time, saving resources during annotation process. Then, a deep-learning classifier is trained and applied for the inspection. The fully automated system, performs well, in various environments, and can be, easily, implemented with most robotic systems.
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Acknowledgments
The research leading to these results has received funding from the EC FP7 project ROBO-SPECT (Contract N.611145). Authors wish to thank all partners within the ROBO-SPECT consortium. The work has been, also, partially supported by IKY Fellowships of excellence for postgraduate studies in Greece-Siemens program.
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Protopapadakis, E., Doulamis, N. (2015). Image Based Approaches for Tunnels’ Defects Recognition via Robotic Inspectors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_63
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DOI: https://doi.org/10.1007/978-3-319-27857-5_63
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