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Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets

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Abstract

Deep Learning methods have important applications in the building construction image classification field. One challenge of this application is Convolutional Neural Networks adoption in a small datasets. This paper proposes a rigorous methodology for tuning of Data Augmentation hyperparameters in Deep Learning to building construction image classification, especially to vegetation recognition in facades and roofs structure analysis. In order to do that, Logistic Regression models were used to analyze the performance of Convolutional Neural Networks trained from 128 combinations of transformations in the images. Experiments were carried out with three architectures of Deep Learning from the literature using the Keras library. The results show that the recommended configuration (Height Shift Range = 0.2; Width Shift Range = 0.2; Zoom Range =0.2) reached an accuracy of \(95.6\%\) in the test step of first case study. In addition, the hyperparameters recommended by proposed method also achieved the best test results for second case study: \(93.3\%\).

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Notes

  1. https://keras.rstudio.com/.

  2. www.pixabay.com.

  3. https://keras.rstudio.com/reference/.

  4. getec.eng.ufba.br.

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Acknowledgements

The authors are grateful to Research Group in Construction Technology and Management (GETEC) - School of Engineering (UFBA) - for providing the second image dataset, the Robotics & Perception Group (University of Zurich) for providing “The Zurich Urban Micro Aerial Vehicle Dataset”, UFBA and UFRB.

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Authors and Affiliations

  1. Technologic and Exact Center, Federal University of Recôncavo da Bahia, Cruz das Almas, Brazil

    André Luiz C. Ottoni

  2. Electrical Engineering Graduate Program, Federal University of Bahia, Salvador, Brazil

    André Luiz C. Ottoni & Raphael M. de Amorim

  3. Department of Electrical and Computer Engineering, Federal University of Bahia, Salvador, Brazil

    Marcela S. Novo

  4. Department of Structural and Construction Engineering, Federal University of Bahia, Salvador, Brazil

    Dayana B. Costa

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  1. André Luiz C. Ottoni
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  2. Raphael M. de Amorim
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Correspondence to André Luiz C. Ottoni.

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Ottoni, A.L.C., de Amorim, R.M., Novo, M.S. et al. Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets. Int. J. Mach. Learn. & Cyber. 14, 171–186 (2023). https://doi.org/10.1007/s13042-022-01555-1

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  • DOI: https://doi.org/10.1007/s13042-022-01555-1

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