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Poster Session I : Next Generation Computing Applications I

Real-Time Interior Object Detection and Segmentation : A Study on the Performance and Applicability of Grounded SAM and FastSAM

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 10th International Conference on Next Generation Computing 2024 (2024.11)바로가기
  • 페이지
    pp.109-112
  • 저자
    Hoijoo Kim, Mingu Kang, Sohyun Park, Eung-Kyo Suh
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A468821

원문정보

초록

영어
As the number of single-person households increases in South Korea, there is a growing demand for more personalized and space-efficient interior design, particularly among the MZ generation who value individuality. Recently, AI-powered services are being developed for efficient interior design. These services utilize indoor photographs to create digital twin-based 3D interior design programs. However, the quality of service varies significantly depending on the algorithm used. In response to this challenge, this study compares and analyzes the image segmentation performance of Grounded SAM and FastSAM, both derived from the Segment Anything Model (SAM) announced by Meta in early 2023. The ADE20K dataset, related to interior design, and the DAVIS2016 dataset, which focuses on single-object segmentation, were used to evaluate the accuracy and processing speed of the two models and to explore their applicability in real-world interior design workflows. The experimental results shows that Grounded SAM outperforms FastSAM in terms of object recognition accuracy. This research will offer valuable criteria for model selection in the automation of interior design and the development of AR/VR applications.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Segment Anything Model (SAM)
B. Grounded SAM
C. FastSAM
III. METHODOLOGY
A. Dataset
B. Performance Evaluation
C. Baseline Models
D. Experimental Design
IV. RESULTS AND DISCUSSION
A. Accuracy Comparison (mIoU)
B. Processing Speed Comparison
C. Practical Evaluation and Model Application Scenarios
V. CONCLUSION
REFERENCES

키워드

Interior Design Object Detection Image Segmentation FastSAM Grounded SAM

저자

  • Hoijoo Kim [ Dept. of Data Science Graduate School, Dankook University ]
  • Mingu Kang [ Dept. of Metaverse Convergencee Graduate School, Dankook University ]
  • Sohyun Park [ Dept. of Software School of SW Convergence, Dankook University ]
  • Eung-Kyo Suh [ Dept. of Data Science Graduate School, Dankook University Yongin, South Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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