Journal of the Korea institute for structural maintenance and inspection (한국구조물진단유지관리공학회 논문집)

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
권호 표지
DOI QR코드

DOI QR Code

Character Recognition of Low Resolution CCTV Images of Sewer Inspection

저해상도 하수관로 CCTV조사 영상의 문자인식

  • 김병철 (한국건설기술연구원 구조융합연구소) ;
  • 최창호 (한국건설기술연구원 지반연구소) ;
  • 손병직 (건양대학교 해외건설플랜트학과)
  • Received : 2016.04.18
  • Accepted : 2016.07.29
  • Published : 2016.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Recent frequent occurrence of urban sinkhole serves as a momentum of the periodic inspection of sewer pipelines. Sewer inspection using a CCTV device needs a lot of time and efforts. Many of previous studies which reduce the laborious tasks are mainly interested in the developments of image processing S/W and inspection H/W. However there has been no attempt to find meaningful information from the existing CCTV images stored by the sewer maintenance manager. This study adopts a cross-correlation based image processing method and extracts location data of sewer inspection device from CCTV images. As a result of the analysis of time-location relation, it shows strong correlation between the device's stand times and the sewer damages. In case of using this method to investigate sewer inspection CCTV images, it will save the investigator's efforts and improve the sewer maintenance efficiency and reliability.

최근 이슈가 되고 있는 도심지 지반함몰로 인하여 주기적인 하수관로 조사의 필요성이 강조되고 있다. 일반적으로 수행되는 조사 방법 중 하나인 하수관로 CCTV조사는 상당한 시간과 노력이 소요된다. 기존 연구들은 주로 하수관로 조사에 소요되는 노력을 줄이기 위한 H/W 및 S/W의 개발에 관한 연구가 주를 이루고 있다. 그러나 기존 CCTV 탐사장치를 이용하여 관리담당자가 보관하고 있는 수많은 조사영상를 활용하기 위한 연구는 진행되지 않았다. 본 연구는 cross-correlation기법 기반의 이미지프로세싱 방법을 적용하여 CCTV 조사영상의 자막으로부터 장치의 위치정보를 추출하였다. CCTV 장치의 시간-거리 관계를 분석한 결과 탐사 장치가 정지시간과 하수관로의 손상 사이의 강한 상관관계를 확인하였다. 제안된 CCTV영상의 분석법을 활용하는 경우 CCTV조사 보고서 작성 및 관리에 소요되는 노력을 줄임으로써 하수관로 유지관리의 효율성과 신뢰도를 높일 수 있을 것으로 기대된다.

Keywords

References

  1. Ahrary, A., Tian, L., Kamata, S., and Ishikawa, M. (2005), An autonomous sewer robots navigation based on stereo camera information, Paper presented at the International Conference on Tools with Artificial Intelligence.
  2. Duran, O., Althoefer, K., and Seneviratne, L. D. (2002), State of the Art in Sensor Technologies for Sewer Inspection, Sensors Journal, IEEE, 2(2), 73-81. https://doi.org/10.1109/JSEN.2002.1000245
  3. Esquivel, S., Koch, R., and Rehse, H. (2009), Reconstruction of Sewer Shaft Profiles from Fisheye-Lens Camera Images, Pattern Recognition (pp. 332-341): Springer.
  4. Ilg, W., Berns, K., Cordes, S., Eberl, M., and Dillmann, R. (1997), A wheeled multijoint robot for autonomous sewer inspection, Paper presented at the Proceedings of the International Conference on Intelligent Robots and Systems.
  5. Kim, B. C., Son, B. J., Choi, C. H., and Park, K. T. (2015), Damage Estimation of Sewer Pipe using Image Processing of the Investigation Video, Paper presented at the Proceedings of Korean Society of Civil Engineers Conference, Kunsan, Republic of Korea.
  6. Lewis, J. P. (1995), Fast normalized cross-correlation, Paper presented at the Vision interface.
  7. MathWorks, I. (2015). Matlab Image Processing Toolbox (Version 2015).
  8. McKim, R. A., and Sinha, S. K. (1999), Condition Assessment of Underground Sewer Pipes using a Modified Digital Image Processing Paradigm, Tunnelling and Underground Space Technology, 14, 29-37.
  9. ME (2011), Standard Manual for Sewer CCTV Inspection and Repair Criterion: Ministry of Environment.
  10. Moselhi, O., and Shehab-Eldeen, T. (1999), Automated Detection of Surface Defects in Water and Sewer Pipes, Automation in Construction, 8(5), 581-588. https://doi.org/10.1016/S0926-5805(99)00007-2
  11. Song, Y. S., and Hwang, H. K. (2013), Image Processing Method for Health Monitoring of Drainpipes, Paper presented at the Proceedings of KSGPC Conference.
  12. Xu, K., Lxmoore, A. R., and Davies, T. (1998), Sewer Pipe Deformation Assessment by Image Analysis of Video Surveys, Pattern Recognition, 31(2), 169-180. https://doi.org/10.1016/S0031-3203(97)00037-X
  13. Yang, M. D., and Su, T. C. (2008), Automated Diagnosis of Sewer Pipe Defects Based on Machine Learning Approaches, Expert Systems with Applications, 35(3), 1327-1337. https://doi.org/10.1016/j.eswa.2007.08.013
  14. Yang, M. D., Su, T. C., Pan, N. F., and Yang, Y. F. (2011), Systematic Image Quality Assessment for SEWER Inspection, Expert Systems with Applications, 38(3), 1766-1776. https://doi.org/10.1016/j.eswa.2010.07.103