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딥러닝 기반 실내 디자인 인식

Deep Learning-based Interior Design Recognition

  • 투고 : 2023.09.11
  • 심사 : 2023.11.15
  • 발행 : 2024.02.28

초록

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

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과제정보

이 논문은 2023년도 정부 (교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임 (No. RS-2023-00241123).

참고문헌

  1. M. Chung, "Lifestyles, Do-it-yourself Interior Design Perception, and Experience Differences of Millennial-Z generation Single-person Households," Journal of the Korean Institute of Interior Design, Vol. 29, No. 4, pp. 21-34, 2020. (in Korean).  https://doi.org/10.14774/JKIID.2020.29.4.021
  2. M. Chung, "Influencing Factors, Resources and Implementation Status of Do-it-yourself Interior Design in Young (20s and 30s) Single-Households in Metropolitan Seoul," Journal of the Korean Institute of Interior Design, Vol. 29, No. 3, pp. 132-142, 2020. (in Korean).  https://doi.org/10.14774/JKIID.2020.29.3.132
  3. R. Girshick, J. Donahue, T. Darrell, J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014. 
  4. R. Girshick, "Fast R-CNN," In Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015. 
  5. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, "You Only Look Once: Unified, Real-time Object Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016. 
  6. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, "SSD: Single Shot Multibox Detector," In European Conference on Computer Vision, Vol. 9905, pp. 21-37, 2016. 
  7. J. Long, E. Shelhamer, T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, 2015. 
  8. O. Ronneberger, P. Fischer, T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," In International Conference on Medical Image Computing and Computer-assisted Intervention Vol. 9351, pp. 234-241, 2015. 
  9. V. Badrinarayanan, A. Kendall, R. Cipolla, "SegNet: A Deep Convolutional Encoder-decoder Architecture for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, pp. 2481-2495, 2017.  https://doi.org/10.1109/TPAMI.2016.2644615
  10. L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 4, pp. 834-848, 2017.  https://doi.org/10.1109/TPAMI.2017.2699184
  11. A. Krizhevsky, I. Sutskever, G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Neural Information Processing Systems, Vol. 25, pp. 1097-1105, 2012. 
  12. K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition," In Proceedings of the International Conference on Learning Representations (ICLR), 2015. 
  13. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going Deeper with Convolutions," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015. 
  14. K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition," Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 770-778, 2016. 
  15. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," arXiv preprint arXiv:2010.11929, 2020. 
  16. M. Tan, Q. Le, "EfficientNet: Rethinking Model Scailing for Convolutional Neural Networks," ICML, Vol. 97, pp. 6105-6114, 2019. 
  17. L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, "Encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation," Proceedings of the European Conference on Computer Vision (ECCV), Vol. 11211, pp. 833-851, 2018. 
  18. F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1800-1807, 2017. 
  19. L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, "Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 4, pp. 834-848, 2017.  https://doi.org/10.1109/TPAMI.2017.2699184
  20. L. C. Chen, G. Papandreou, F. Schroff, H. Adam, "Rethinking Atrous Convolution for Semantic Image Segmentation," arXiv Preprint arXiv:1706.05587, 2017. 
  21. K. He, X. Zhang, S. Ren, J. Sun, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 9, pp. 1904-1916, 2015.  https://doi.org/10.1109/TPAMI.2015.2389824
  22. Y. Cui, M. Jia, T. Y. Lin, Y. Song, S. Belongie, "Class-Balanced Loss Based on Effective Number of Samples," Computer Vision and Pattern Recognition, pp. 9260-9269, 2019. 
  23. H. Robbins, S. Monro, "A Stochastic Approximation Method," Annals of Mathematical Statistics, Vol. 22, pp. 400-407, 1951.  https://doi.org/10.1214/aoms/1177729586
  24. L. G. Valiant, "A Theory of the Learnable," Communications of the ACM, Vol. 27, No. 11, pp. 1134-1142, 1984. https://doi.org/10.1145/1968.1972