[논문]딥러닝 기반 실내 디자인 인식

이원규; 박지훈; 이종혁; 정희철

doi:10.14372/iemek.2024.19.1.47

[국내논문] 딥러닝 기반 실내 디자인 인식
Deep Learning-based Interior Design Recognition

대한임베디드공학회논문지 = IEMEK Journal of embedded systems and applications, v.19 no.1, 2024년, pp.47 - 55

이원규 (Kyungpook National Univ.) , 박지훈 (Kyungpook National Univ.) , 이종혁 (Seoreu Co., Inc) , 정희철 (Kyungpook National Univ.)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (16)

그림 그림 1. 인테리어 분석 시스템의 전체 아키텍쳐 Fig. 1. Overall Architecture of our interior analysis system
그림 그림 2. DeepLab V3+ 모델 구조 Fig. 2. An illustration of the DeepLab V3+ [17]
그림 그림 3. 마스킹 처리한 이미지 예시 Fig. 3. Example of masked Image
표 표 1. 의미적 분할 모델 데이터 셋 레이블 리스트 Table 1. Semantic segmentation model dataset label list
그림 그림 4. 의미적 분할 클래스별 데이터 분포 Fig. 4. Distribution of data by semantic segmentation class
그림 그림 5. 속성 클래스별 데이터 분포 Fig. 5. Distribution of data by attribute class
그림 그림 6. 데이터의 좌표 시각화 이미지 Fig 6. Example of coordinate visualization
그림 그림 7. 속성 분류 예시 이미지 Fig. 7. Attribute classification example image
그림 그림 8. 의미적 분할 모델 과적합 예시: 왼쪽부터 6 epoch, 10 epoch, 15 epoch, 20 epoch 에서의 모델 예측값이다. Fig. 8. Example of semantic segmentation model overfitting: from left to right: model prediction values at 6 epoch, 10 epoch, 15 epoch, and 20 epoch.
그림 그림 9. 의미적 분할 모델 mIoU 성능 그래프 Fig. 9. Semantic Segmentation Model mIoU performance graph
그림 그림 10. 속성별 기존 모델과 제안 기법 적용 모델의 혼동 행렬 Fig. 10. Confusion Matrix of Attribute-based Baseline Model and Proposed technique applied Model
그림 그림 11. 의미적 분할 모델의 예측 결과 샘플: 왼쪽부터 입력 이미지, GT (ground truth), 모델 예측값이다. Fig. 11. Sample of semantic segmentation model prediction results: from left to right: input image, GT (ground truth), and model prediction
표 표 2. Backbone에 따른 모델 성능 Table 2. Model performance according to the backbone.
표 표 3. 각 클래스에 해당하는 RGB 값 리스트 Table 3. List of RGB values corresponding to each class
표 표 4. CNN 모델별 정확도 성능 Table 4. Performance by CNN model
표 표 5. 제안 기법별 EfficientNet 정확도 성능 Table 5. Performance of EfficientNet by Proposed method

참고문헌 (24)

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).？

상세보기
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).？

상세보기
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.？
R. Girshick, "Fast R-CNN," In Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.？
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.？
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.？
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.？
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.？
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.？

상세보기
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.？
A. Krizhevsky, I. Sutskever, G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Neural Information Processing Systems, Vol. 25, pp. 1097-1105, 2012.？
K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition," In Proceedings of the International Conference on Learning Representations (ICLR), 2015.？
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.？
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.？
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.？
M. Tan, Q. Le, "EfficientNet: Rethinking Model Scailing for Convolutional Neural Networks," ICML, Vol. 97, pp. 6105-6114, 2019.？
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.？
F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1800-1807, 2017.？
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.？
L. C. Chen, G. Papandreou, F. Schroff, H. Adam, "Rethinking Atrous Convolution for Semantic Image Segmentation," arXiv Preprint arXiv:1706.05587, 2017.？
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.？

상세보기
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.？
H. Robbins, S. Monro, "A Stochastic Approximation Method," Annals of Mathematical Statistics, Vol. 22, pp. 400-407, 1951.？

상세보기
L. G. Valiant, "A Theory of the Learnable," Communications of the ACM, Vol. 27, No. 11, pp. 1134-1142, 1984.

상세보기

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증