Journal of the Society of Disaster Information (한국재난정보학회 논문집)

The Korean Society of Disaster Information (한국재난정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Study of Railway Bridge Automatic Damage Analysis Method Using Unmanned Aerial Vehicle and Deep Learning-based Image Analysis Technology

무인이동체와 딥러닝 기반 이미지 분석 기술을 활용한 철도교량 자동 손상 분석 방법 연구

  • Received : 2021.08.02
  • Accepted : 2021.09.08
  • Published : 2021.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

연구목적: 본 연구에서는 무인이동체를 활용한 철도교량의 외관조사 점검을 보다 효율적이고 신뢰성 있게 점검을 위하여 무인이동체를 통해 촬영된 이미지를 바탕으로 다양한 방식의 딥러닝 기반 자동 손상 분석기술을 검토하였다. 연구방법: 취득된 이미지를 바탕으로 손상항목을 정의하고 학습데이터로 추출하여 딥러닝 분석 모델을 생성하였다. 그리고 철도교량의 외관 손상 중 균열, 콘크리트 박리·박락, 누수, 철근노출에 대한 손상 이미지를 학습한 모델을 적용하여 자동 손상 분석 결과로 테스트하였다. 연구결과: 분석 결과 평균 95%이상 검측 재현율을 도출하는 분석 기법을 검토할 수 있었다. 이와 같은 분석 기술은 기존 육안점검 결과 대비 보다 객관적이고 정밀한 손상 검측이 가능하다. 결론: 본 연구를 통해 개발된 기술을 통해 철도 유지관리 분야에서 무인이동체를 활용한 정기점검 시 자동손상분석을 통한 객관적인 결과도출과 기존 대비 소요시간, 비용저감이 가능할 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원 공공혁신조달연계 무인이동체 및 SW플랫폼 개발사업의 연구비지원 (무인이동체기반 접근취약 철도시설물 자동화점검시스템 개발)에 의해 수행되었습니다.

References

  1. Abdel-Qader, I., Abudayyeh, O., Kelly, M.E. (2003). "Analysis of edge-detection techniques for crack identification in bridges." Journal of Computing in Civil Engineering, Vol. 17, No. 4, pp. 255-263. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255)
  2. Amhaz, R., Chambon, S., Idier, J., Baltazart, V. (2016). "Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection." IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 10, pp. 2718-2729. https://doi.org/10.1109/TITS.2015.2477675
  3. Chambon, S., Moliard, J.M. (2011). "Automatic road pavement assessment with image processing: Review and comparison." International Journal of Geophysics, Vol. 2011, pp. 556-575.
  4. Chan, B., Guan, H., Jo, J., Blumenstein, M. (2015). "Towards UAV-based bridge inspection systems: A review and an application perspective." Structural Monitoring and Maintenance, Vol. 2, No. 3, pp. 283-300. https://doi.org/10.12989/smm.2015.2.3.283
  5. Chanda, S., Bu, G., Guan, H., Jo, J., Pal, U., Loo, Y., Blumenstein, M. (2014). "Automatic bridge crack detection - a texture analysis-based approach." IAPR Workshop on Artificial Neural Networks in Pattern Recognition, Montreal, Canada, pp. 193-203.
  6. De Melo, R.R., Costa, D.B., Alvares, J.S., Irizarry, J. (2017). "Applicability of unmanned aerial system (UAS) for safety inspection on construction sites." Safety Science, Vol. 98, pp. 174-185. https://doi.org/10.1016/j.ssci.2017.06.008
  7. Gavilan, M., Balcones, D., Marcos, O., Llorca, D.F., Sotelo, M.A., Parra, I., Amirola, A. (2011). "Adaptive road crack detection system by pavement classification." Sensors, Vol. 11, No. 10, pp. 9628-9657. https://doi.org/10.3390/s111009628
  8. Giakoumis, I., Nikolaidis, N., Pitas, I. (2005). "Digital image processing techniques for the detection and removal of cracks in digitized paintings." IEEE Transactions on Image Processing, Vol. 15, No. 1, pp. 178-188. https://doi.org/10.1109/TIP.2005.860311
  9. Irizarry, J., Gheisari, M., Walker, B.N. (2012). "Usability assessment of drone technology as safety inspection tools." Journal of Information Technology in Construction (ITcon), Vol. 17, No. 12, pp. 194-212.
  10. Jahanshahi, M.R., Kelly, J.S., Masri, S.F., Sukhatme, G.S. (2009). "A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures." Structure and Infrastructure Engineering, Vol. 5, No. 6, pp. 455-486. https://doi.org/10.1080/15732470801945930
  11. Kaseko, J.M., Ritchie, S.G. (1993). "A neural network-based methodology for pavement crack detection and classification." Transportation Research Part C: Emerging Technologies, Vol. 1, No. 4, pp. 275-291. https://doi.org/10.1016/0968-090X(93)90002-W
  12. Kim, M., Kim, K., Choi, S. (2017) "Development of automatic crack identification algorithm for a concrete sleeper using pattern recognition." Journal of the Korean Society for Railway, Vol. 20, No. 3, pp. 374-381. https://doi.org/10.7782/JKSR.2017.20.3.374
  13. Lee, B.J., Shin, D.H., Seo, J.W., Jung, J.D., Lee, J.Y. (2011). Intelligent Bridge Inspection using Remote Controlled Robot and Image Processing Technique. International Symposium on Automation and Robotics in Construction (ISARC), Seoul, Korea, pp. 1426-1431.
  14. Lee, H.B., Kim, J.W., Jang, I.Y. (2012). "Development of automatic crack detection system for concrete structure using image processing method." Journal of the Korea institute for structural maintenance and inspection, Vol. 16, No. 1, pp. 64-77. https://doi.org/10.11112/JKSMI.2012.16.1.064
  15. Long, J., Shelhamer, E., Darrell, T. (2015). "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440.
  16. Lovelace, B., Zink, J. (2015). Unmanned Aerial Vehicle Bridge Inspection Demonstration Project. Research Project. Final Report, Minnesota Department of Transportation Research Services & Library, USA, pp. 40.
  17. Lu, M., Hu, Y., Lu, X. (2019). "Dilated Light-Head R-CNN using tri-center loss for driving behavior recognition." Image and Vision Computing, Vol. 90, pp. 103800. https://doi.org/10.1016/j.imavis.2019.08.004
  18. Miyamoto, A., Konno, M., Bruhwiler, E. (2007). "Automatic crack recognition system for concrete structures using image processing approach." Asian Journal of Information Technology, Vol. 6, No. 5, pp. 553-561.
  19. Na, Y.H., Park, M.Y., Park, J.S., Park, S.B., Kwon, S.G. (2018). "Development of automatic crack detection using the Gabor Filter for concrete structures of railway tracks." Journal of the Society of Disaster Information, Vol. 14, No. 4, pp. 458-465.
  20. Ronneberger, O., Fischer, P., Brox, T. (2015). "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, Cham, Munich, Germany, pp. 234-241.