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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.2
- 수록면
- 59 - 74 (16page)
- DOI
- 10.7848/ksgpc.2023.41.1.59
이용수
초록· 키워드
오류제보하기
The continuous increase in interest in indoor space demands the development of LBSs (Location Based Services) for navigation, routing, and space query in these environments. Applications providing these services must use visualization and topology data together to provide relevant services to users. Applications have frequently used geometric object-based visualization models for indoor space LBSs. These have the advantage of being easy to use with IndoorGML, an indoor topology model, but have the disadvantage of high construction cost and heavy data. Correspondingly, the image-based visualization model drew attention as an alternative model. However, using such models requires identifying objects on the image to be used alongside IndoorGML, presenting a limitation because it is difficult to identify objects in the image pixels directly. To overcome the limitations of image-based visualization models and reconsider the usability of indoor space LBS using images, this study presents a method to automatically detect spatial objects required to construct IndoorGML from images using deep learning. This methodology aims to detect objects mainly used in indoor LBSs, which include door and stair objects portrayed in indoor omnidirectional images. This study proposes a detailed method of constructing a training dataset for indoor spatial object detection. Moreover, this research presents a method of refining the training dataset and directly acquiring omnidirectional image data to train it as an object detection model that can be used universally in various buildings while maintaining compliant accuracy.
목차
Abstract
1. Introduction
2. Related Works
3. Methodology
4. Experimental Implementation
5. Conclusion
References
참고문헌 (24)
참고문헌 신청
Afif, M., Ayachi, R., Pissaloux, E., Said, Y., and Atri, M.(2020), Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people, Multimedia Tools and Applications, Vol. 79, pp.31645-https://doi.org/10.1007/s11042-020
Ahn, D., Claridades, A.R.C., and Lee, J. (2020), Integrating image and network-based topological data through spatial data fusion for indoor location-based services, Journal of Sensors, Vol. 2020, Article ID 8877739, 12 pages.https://doi.org/10.1155/2020/
Bochkovskiy, A., Wang, C., and Liao, H.M. (2020), YOLOv4: Optimal speed and accuracy of object detection, arXiv:2004.10934v1.
Claridades, A.R.C., Ahn, D., and Lee, J. (2019), Developing a data fusion sterategy between omnidirctional image and IndoorGML data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), 14-15 November,
Claridades, A.R.C., Lee, J., and Blanco, A. (2018a), 3D visualization of building interior using omnidirectional images, 39th Asian Conference on Remote Sensing, 15-19October, Kuala Lumpur, Malaysia, pp. 2244-2253.
Ahn, D., Claridades, A.R.C., and Lee, J. (2020), Integrating image and network-based topological data through spatial data fusion for indoor location-based services, Journal of Sensors, Vol. 2020, Article ID 8877739, 12 pages.https://doi.org/10.1155/2020/
Bochkovskiy, A., Wang, C., and Liao, H.M. (2020), YOLOv4: Optimal speed and accuracy of object detection, arXiv:2004.10934v1.
Claridades, A.R.C., Ahn, D., and Lee, J. (2019), Developing a data fusion sterategy between omnidirctional image and IndoorGML data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), 14-15 November,
Claridades, A.R.C., Lee, J., and Blanco, A. (2018a), 3D visualization of building interior using omnidirectional images, 39th Asian Conference on Remote Sensing, 15-19October, Kuala Lumpur, Malaysia, pp. 2244-2253.
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