Segmentation of Floorplans and Heritage Sites: An Approach to Unbalanced Dataset

Authors

  • Sujith Gunjur Umapathy SRH University Berlin, Charlottenburg, Germany
  • Alexander I. Iliev SRH University Berlin, Charlottenburg, Germany; Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

DOI:

https://doi.org/10.55630/dipp.2022.12.17

Keywords:

Deep Learning, Unbalanced Dataset, Semantic Segmentation, Focal Loss, Jaccard Loss

Abstract

To identify structural deficiencies, floorplan digitization is essential for legacy buildings, and historically significant and culturally rich sites. However, these technologies heavily depend on vector graphics. For an efficient and economical solution, we present an approach with multi-loss functions to handle unbalanced classes in floorplans using U-Net architecture.

References

BigRentz, Inc. (2022, July 10). How to Understand Floor Plan Symbols . Retrieved from BigRentz: https://www.bigrentz.com/blog/floor-plan-symbols

Carniato, L. (2021, April 6). Multi-Class classification using Focal Loss and LightGBM . Retrieved from Towards Datascience: https://towardsdatascience.com/multi-class-classification-using-focal-loss-andlightgbm-a6a6dec28872

Consortium, C. (2015). COCO - Common Objects in Context . Retrieved from COCO - Common Objects in Context: https://cocodataset.org/#home de las Heras, L.-P., Terrades, O., Robles, S., & S’anchez, G. (2015). CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. International Journal on Document Analysis and Recognition .

Dodge, S., Xu, J., & Stenger, B. (2017). Parsing floor plan images. 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) , 358-361.

Kalervo, A., Ylioinas, J., Häikiö, M., Karhu, A., & Kannala, J. (2019). CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis. arXiv.

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. arXiv.

Liu, C., Wu, J., Kohli, P., & Furukawa, Y. (2017). Raster-to-Vector: Revisiting Floorplan Transformation. International Conference on Computer Vision (ICCV) (pp. 2214-2222). 2017 IEEE.

Moore, C., & Bell, D. (2020, March 15). Dice similarity coefficient. Reference article . Retrieved from https://doi.org/10.53347/rID-75056

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) .

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research , 1929-1958.

van Beers, F., Lindstrm, А., Okafor, Е., & Wiering, М . (2019). Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (pp. 438--445).

Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series , 022022.

Zaworski, R. (2021, February 14). Medium . Retrieved from Medium: https://medium.com/snowdog-labs/data-augmentation-techniques-and-pitfalls-ofsmall-datasets-e5a657fc404f

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Published

2022-09-07

How to Cite

Gunjur Umapathy, S., & I. Iliev, A. (2022). Segmentation of Floorplans and Heritage Sites: An Approach to Unbalanced Dataset. Digital Presentation and Preservation of Cultural and Scientific Heritage, 12, 205–216. https://doi.org/10.55630/dipp.2022.12.17

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