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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
- 2023.10
- 수록면
- 351 - 365 (15page)
- DOI
- 10.7848/ksgpc.2023.41.5.351
이용수
초록· 키워드
오류제보하기
Spatial data is important for virtually representing the real world and is essential in developing applications for making informed decisions. With the growing interest in seamless indoor-outdoor environments, spatial data from different sources exists in various formats for use in LBS (Location-Based Services). Previous research has utilized deep learning for indoor omnidirectional images to generate NRS (Node-Relation Structure), a network-based topological data, for supporting spatial analysis for navigation while providing visualization. This study proposes an approach to detect building entrances in street view omnidirectional images through a deep learning-based object detection algorithm for supporting indoor-outdoor LBS. This paper focuses on formulating refinement conditions for constructing an image training dataset that combines both an open dataset and directly captured omnidirectional images to address the challenge of establishing a huge volume of images for training the object detection model. By applying the conditions, the mAP (mean Average Precision) of 61.20% obtained from training with open data increased to 85.72%, and applying image augmentation methods improved the mAP to 87.42%. These results show that the proposed conditions can be used as a framework for constructing generalized training data that results in accurate entrance detection in street view images, regardless of the study area.
목차
Abstract
1. Introduction
2. Related Literature
3. Methodology
4. Experimental Implementation
5. Conclusions and Recommendations
References
참고문헌 (41)
참고문헌 신청
Ahn, D., Claridades, A.R. 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, No. Advanced Sensor Technologies in Geospatial Sciences and
Ahn, D., Park, J., and Lee, J. (2018), “Defining geospatial data fusion methods based on topological relationships:” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 4/W9, pp. 317
Barnefske, E. and Sternberg, H. (2022), “Evaluating the Quality of Semantic Segmented 3D Point Clouds:” Remote Sensing, Vol. 14, No. 3.https://doi.org/10.3390/rs14030446
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020), “YOLOv4: Optimal Speed and Accuracy of Object Detection:” Computing Research Repository (CoRR), Vol.2020https://doi.org/https://doi.org/10.48550/arXiv.2004.10934
Claridades, A.R., Ahn, D., and Lee, J. (2019), “Developing a Data Fusion Strategy between Omnidirectional Image and IndoorGML Data:” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. XLII,
Ahn, D., Park, J., and Lee, J. (2018), “Defining geospatial data fusion methods based on topological relationships:” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 4/W9, pp. 317
Barnefske, E. and Sternberg, H. (2022), “Evaluating the Quality of Semantic Segmented 3D Point Clouds:” Remote Sensing, Vol. 14, No. 3.https://doi.org/10.3390/rs14030446
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020), “YOLOv4: Optimal Speed and Accuracy of Object Detection:” Computing Research Repository (CoRR), Vol.2020https://doi.org/https://doi.org/10.48550/arXiv.2004.10934
Claridades, A.R., Ahn, D., and Lee, J. (2019), “Developing a Data Fusion Strategy between Omnidirectional Image and IndoorGML Data:” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. XLII,
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