DOI QR코드

DOI QR Code

Rectification of Smartphone Image Based on Reference Images for Facility Monitoring

시설물 모니터링을 위한 기준영상 기반 스마트폰 영상의 기하보정

  • Kim, Hwiyoung (Department of Geoinformatics, University of Seoul) ;
  • Choi, Kyoungah (Department of Geoinformatics, University of Seoul) ;
  • Lee, Impyeong (Department of Geoinformatics, University of Seoul) ;
  • Yoon, Hyuk-Jin (ICT-Railroad Convergence Research Team, Korea Railroad Research Institute)
  • 김휘영 (서울시립대학교 공간정보공학과) ;
  • 최경아 (서울시립대학교 공간정보공학과) ;
  • 이임평 (서울시립대학교 공간정보공학과) ;
  • 윤혁진 (한국철도기술연구원 ICT융합신기술연구팀)
  • Received : 2017.04.10
  • Accepted : 2017.04.26
  • Published : 2017.04.30

Abstract

Monitoring of facilities such as roads, dams and bridges is important for their long-term sustainable usage. It has usually suffered with safety and cost problems, which makes more frequent monitoring difficult. As an efficient and economicalsolution to these problems, one may consider the use of smartphone to capture the status of the facilities. To derive quantitative analysis results with the smartphone images for facility monitoring, one should first rectify the images in a way as automatic and economical as possible. In thisstudy, we propose such a rectification method, which rectifiessmartphone images acquired from arbitrary locations based on reference images.In the proposed method, we determine the camera extrinsic parameters of each smartphone images using the reference imagesrather than ground control points, and project the image to the target surface of the facility based on the determined camera parameters. The method were applied to test data acquired from a small dam toward water-area facility monitoring. The experimental results showed that the camera extrinsic parameters were determined with the accuracy of 5 cm and $0.28^{\circ}$ in the position and attitude. The accuracy of the distance measured from the rectified image was evaluated to 10 cm. With the rectified images, one can accurately determine the location and length of the target objects required for facility monitoring.

시설물의 장기적이고 지속적인 사용을 위해서 모니터링은 중요하다. 특히, 도로, 댐, 교량 등의 시설물은 안전과 비용의 문제로 자주 점검하기 어렵다. 이러한 문제에 대한 효율적이고 경제적인 대안으로 스마트폰 기반 모니터링을 고려할 수 있다. 시설물 모니터링을 위해 스마트폰 영상을 정량적으로 분석하기 위해서, 먼저 절대좌표계를 기준으로 보정해야 한다. 본 연구는 임의의 위치에서 취득된 스마트폰 영상을 기준영상을 기반으로 보정하여 시설물 모니터링에 활용하는 방법을 제시한다. 기준영상을 활용하여 지오레퍼런싱을 수행하여 스마트폰의 외부표정요소를 결정한다. 이를 이용하여 스마트폰 영상을 시설물의 대상 객체면에 투영하여 보정한다. 제안된 방법은 보에서 취득한 시험데이터에 적용하였다. 스마트폰의 외부표정요소는 위치 5 cm, 자세 $0.28^{\circ}$ 정확도로 결정되었다. 보정된 스마트폰 영상에서 측정한 길이는 10 cm의 오차를 보였다. 제안된 방법을 이용하여 스마트폰 영상을 시설물 모니터링에 효과적으로 활용할 수 있을 것으로 기대된다.

Keywords

References

  1. Atzori, L., T. Dessi, and V. Popescu 2012. Indoor navigation system using image and sensor data processing on a smartphone, Proc. of 2012 13th International Conference on IEEE Optimization of Electrical and Electronic Equipment, Romania, May 24-26, pp. 1158-1163.
  2. Choi, K. and I. Lee, 2016. Accuracy Verification on the Facility Monitoring through a Low-altitude Unmanned Aerial Survey, Proc. of 2016 Korean Conference of Hazard Mitigation, Seoul, Korea, Feb. 19, pp. 140.
  3. Dworakowski, Z., P. Kohut, A. Gallina, K. Holak, and T. Uhl, 2016. Vision-based algorithms for damage detection and localization in structural health monitoring, Structural Control and Health Monitoring, 23(1): 35-50. https://doi.org/10.1002/stc.1755
  4. Feng, D. and M.Q. Feng, 2015. Vision-based multipoint displacement measurement for structural health monitoring, Structural Control and Health Monitoring, 23(5): 876-890. https://doi.org/10.1002/stc.1819
  5. Fukuda, Y., M.Q. Feng, Y. Narita, S.I. Kaneko, and T. Tanaka, 2013. Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm, IEEE Sensors Journal, 13(12): 4725-4732. https://doi.org/10.1109/JSEN.2013.2273309
  6. Google, 2016. Mobile Apps in APAC: 2016 Report, Google. https://apac.thinkwithgoogle.com/intl/en/articles/mobile-apps-in-apac-2016-report.html
  7. Han, S., J. Park, and W. Lee, 2016. On-Site vs. Laboratorial Implementation of Camera Self-Calibration for UAV Photogrammetry, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 34(4): 349-356. https://doi.org/10.7848/ksgpc.2016.34.4.349
  8. Kim, H., K. Choi, I. Lee, and E. Jeon, 2013. A pilot study toward image based determination of smartphone's position and attitude by fast sequential bundle adjustment, Proc. of International Symposium on Mobile Mapping Technology 2013, Tainan, Taiwan, May 1-3.
  9. Kim, H.G., H.S. Yun, and J.M. Cho, 2015. Analysis of 3D Accuracy According to Determination of Calibration Initial Value in Close-Range Digital Photogrammetry Using VLBI Antenna and Mobile Phone Camera. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(1): 31-43 (in Korean with English abstract). https://doi.org/10.7848/ksgpc.2015.33.1.31
  10. Malesa, M. and M. Kujawinska, 2013. Deformation measurements by digital image correlation with automatic merging of data distributed in time, Applied Optics, 52(19): 4681-4692. https://doi.org/10.1364/AO.52.004681
  11. McGlone, C., 2004. Manual of Photogrammetry, 5th Edition, ASPRS, Bethesda, Maryland, USA, pp. 847-870.
  12. Metni, N. and Hamel, T., 2007. A UAV for bridge inspection: Visual servoing control law with orientation limits, Automation in Construction, 17(1): 3-10. https://doi.org/10.1016/j.autcon.2006.12.010
  13. Morgenthal, G. and N. Hallermann,, 2014. Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures, Advances in Structural Engineering, 17(3): 289-302. https://doi.org/10.1260/1369-4332.17.3.289
  14. Nikolic, J., M. Burri, J. Rehder, S. Leutenegger, C. Huerzeler, and R. Siegwart, 2013. A UAV system for inspection of industrial facilities, Proc. Of 2013 IEEE Aerospace Conference, Big Sky, MT, USA, Mar. 2-9, pp. 1-8.
  15. Sladek, J., K. Ostrowska, P. Kohut, K. Holak, A. Gaska, and T. Uhl, 2013. Development of a vision based deflection measurement system and its accuracy assessment, Measurement, 46(3): 1237-1249. https://doi.org/10.1016/j.measurement.2012.10.021
  16. Sui, W. and K. Wang, 2015. An accurate indoor localization approach using cellphone camera. Proc. of 2015 11th International Conference on Natural Computation, Zhangjiajie, China, Aug. 15-17, pp. 949-953.
  17. Werner, M., M. Kessel, and C. Marouane, 2011. Indoor positioning using smartphone camera, Proc. of 2011 International Conference on IEEE Indoor Positioning and Indoor Navigation, Portugal, Sep. 21-23, pp. 1-6.
  18. Xue, H., L. Ma, and X. Tan, 2016. A fast visual map building method using video stream for visualbased indoor localization, Proc. of 2016 International Wireless Communications and Mobile Computing Conference, Paphos, Cyprus, Sep. 5-9, pp. 650-654.
  19. Ye, X.W., C.Z. Dong, and T. Liu, 2016. Image-based structural dynamic displacement measurement using different multi-object tracking algorithms, Smart Structures and Systems, 17(6): 935-956. https://doi.org/10.12989/sss.2016.17.6.935