딥러닝 기반 실내 디자인 인식 (Deep Learning-based Interior Design Recognition)

서지정보

· 발행기관 : 대한임베디드공학회
· 수록지 정보 : 대한임베디드공학회논문지 / 19권 / 1호 / 47 ~ 55페이지
· 저자명 : 이원규, 박지훈, 이종혁, 정희철

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.

영어초록

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.

참고자료

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