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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 1,633 - 1,636 (4page)
- DOI
- 10.7840/kics.2023.48.12.1633
이용수
초록· 키워드
오류제보하기
Recently, persistent catastrophic issues and advancements in science and technology have increased the need for disaster research. In this study, we propose a deep learning-based framework that distinguishes disaster images from large-scale datasets, thereby providing access to disaster-related image data. To construct an accurate dataset for our framework, disaster images were manually collected and labeled from various open datasets. Image generation and augmentation techniques were used to supplement the insufficient training dataset and enhance the classifier training of our classification framework. We built a classification framework that demonstrates over 99% accuracy in classification experiments using open datasets.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Proposed method
Ⅲ. Experiments
Ⅳ. Conclusion
References
참고문헌 (9)
참고문헌 신청
L. Zhou, Y. Zhou, J. J. Corso, R. Socher, and C. Xiong, “End-to-end dense video captioning with masked transformer,” in Proc. IEEE Conf. CVPR, pp. 8739-8748, Utah, USA, Jun. 2018. (https://doi.org/10.48550/arXiv.1804.00819)
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, and G. Krueger, “Learning transferable visual models from natural language supervision,” ICML, pp. 8748-8763, Feb. 2021. (https://doi.org/10.4855
T. Kim, J. Bang, J. Seo, and K. Sohn, “Object detection for Fire Disaster Situation Recognition,” in Proc. Annual Conf. of KIPS 2022(ACK 2022), pp. 426-428, Chuncheon, Korea, Nov. 2022. (https://doi.org/10.3745/PKIPS.y2022m11a.426)
T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” Computer Vision–ECCV 2014, pp. 740-755, Zurich, Switzerland, Sep. 2014. (https://doi.org/10.1007/978-3-319-10602-1
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Popov, M. Malloci, A. Kolesnikov, T. Duerig, and V. Ferrari, “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at sca
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, and G. Krueger, “Learning transferable visual models from natural language supervision,” ICML, pp. 8748-8763, Feb. 2021. (https://doi.org/10.4855
T. Kim, J. Bang, J. Seo, and K. Sohn, “Object detection for Fire Disaster Situation Recognition,” in Proc. Annual Conf. of KIPS 2022(ACK 2022), pp. 426-428, Chuncheon, Korea, Nov. 2022. (https://doi.org/10.3745/PKIPS.y2022m11a.426)
T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” Computer Vision–ECCV 2014, pp. 740-755, Zurich, Switzerland, Sep. 2014. (https://doi.org/10.1007/978-3-319-10602-1
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Popov, M. Malloci, A. Kolesnikov, T. Duerig, and V. Ferrari, “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at sca
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