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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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기사명 저자명 페이지 원문 목차
카메라 기반 객체의 위치인식을 위한 왜곡제거 및 오검출 필터링 기법 = Distortion removal and false positive filtering for camera-based object position estimation 진실, 송지민, 최지호, 진용식, 정재진, 이상준 p. 1-8

3차원 자세 추정 기법의 성능 향상을 위한 임의 시점 합성 기반의 고난도 예제 생성 = Hard example generation by novel view synthesis for 3-D pose estimation 김민지, 김성찬 p. 9-17

영상데이터의 개인정보 영역에 대한 인공지능 기반 비식별화 기법 연구 = Research on artificial intelligence based de-identification technique of personal information area at video data 송인준, 김차종 p. 19-25

저가 카메라를 이용한 스마트 장난감 게임을 위한 모형 자동차 인식 = Recognition of model cars using low-cost camera in smart toy games 강민혜, 홍원기, 고재필 p. 27-32

인공지능 드론 배송 시스템의 구현 및 검증 = Implementation and verification of artificial intelligence drone delivery system 이성남 p. 33-38

다수의 IR-UWB 레이다를 이용한 인원수 및 좌표 추정 연구 = People counting and coordinate estimation using multiple IR-UWB radars 김태윤, 윤세원, 최인오, 정주호, 박상홍 p. 39-46

딥러닝 기반 실내 디자인 인식 = Deep learning-based interior design recognition 이원규, 박지훈, 이종혁, 정희철 p. 47-55

음향 데이터를 이용한 CNN 추론 윈도우 기반 산업용 직교 좌표 로봇의 고장 진단 기법 = Failure detection method of industrial cartesian coordinate robots based on a CNN inference window using ambient sound 조현태 p. 57-64

다중 채널 동적 객체 정보 추정을 통한 특징점 기반 Visual SLAM = A new feature-based visual SLAM using multi-channel dynamic object estimation 박근형, 조형기 p. 65-71

참고문헌 (24건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
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). 미소장
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). 미소장
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. 미소장
4 R. Girshick, "Fast R-CNN,” In Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
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. 미소장
16 M. Tan, Q. Le, "EfficientNet: Rethinking Model Scailing for Convolutional Neural Networks,” ICML, Vol. 97, pp. 6105-6114, 2019. 미소장
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. 미소장
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. 미소장
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. 미소장
20 L. C. Chen, G. Papandreou, F. Schroff, H. Adam, "Rethinking Atrous Convolution for Semantic Image Segmentation," arXiv Preprint arXiv:1706.05587, 2017. 미소장
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. 미소장
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. 미소장
23 H. Robbins, S. Monro, "A Stochastic Approximation Method,” Annals of Mathematical Statistics, Vol. 22, pp. 400-407, 1951. 미소장
24 L. G. Valiant, "A Theory of the Learnable," Communications of the ACM, Vol. 27, No. 11, pp. 1134-1142, 1984. 미소장

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