Abstract
The automatic detection of structure defects based on computer vision is evolving, especially with constant advances in Deep Convolutional Neural Network. Several image-processing methods have been proposed over the years based on Deep Learning. Nevertheless, the studies are mainly concerned with the crack damage and do not takes into account the other pathologies that can affect a surface structure such as Alkali-silica reaction (ASR), efflorescence, carbonation of concrete, friable plaster, water infiltration, and scaling. In this paper, we propose the method of pre-trained learning Deep Convolutional Neural Networks DCNN model with Transfer learning for the detection of seven classes of old building damage in Medina of Fez and Meknes in Morocco. The robustness of the proposed approach is tested on different architectures and a small set of images not used in learning and validation steps.
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Acknowledgments
We thank our partners from Structural Engineering Research and Consulting Office “Structal Engineering Bureau d’études & Conseil”, Fez Agency 134 B Lot Riad Azzaitoun R.A.C, Etg1, Route Ain Chkef 30000 Fez-Morocco, and our partners from NBR-North Center Laboratory of Building and Public Works, 29 Rue Abdessalam Mestaoui, quartier Adarissa, VN Fez- Morocco, who provided insight, expertise and assistance with defects identification and relevance that greatly assisted our research.
This work was supported by CMCLI Project, funded by My Ismaïl University Meknes.
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Masrour, T., El Hassani, I., Bouchama, M.S. (2020). Deep Convolutional Neural Networks with Transfer Learning for Old Buildings Pathologies Automatic Detection. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1104. Springer, Cham. https://doi.org/10.1007/978-3-030-36671-1_18
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DOI: https://doi.org/10.1007/978-3-030-36671-1_18
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