한국항해항만학회지 (Journal of Navigation and Port Research)

  • 제46권3호
  • /
  • Pages.243-250
  • /
  • 2022
  • /
  • 1598-5725(pISSN)
  • /
  • 2093-8470(eISSN)
한국항해항만학회 (Korean Institute of Navigation and Port Research)
권호 표지
DOI QR코드

DOI QR Code

합성곱 신경망(CNN) 기반 실시간 월파 감지 및 처오름 높이 산정

Real-time Wave Overtopping Detection and Measuring Wave Run-up Heights Based on Convolutional Neural Networks (CNN)

  • 성보람 ((주)해양정보기술 해양정보연구소) ;
  • 조완희 ((주)해양정보기술) ;
  • 문종윤 ((주)해양정보기술) ;
  • 이광호 (한국해양대학교 물류.환경.도시인프라공학부 건설공학전공)
  • 투고 : 2022.02.22
  • 심사 : 2022.05.11
  • 발행 : 2022.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 인공지능을 활용한 영상분석 기술을 통해 영상 내의 월파를 실시간으로 감지하고 처오름 높이를 산정하는 기술을 제안하였다. 본 연구에서 제안한 월파 감지 시스템은 실시간으로 악기상 및 야간에도 월파를 감지할 수 있음을 확인하였다. 특히, 합성곱 신경망을 적용하여 실시간으로 CCTV 영상에서 파랑의 처오름을 감지하고 월파 여부를 판단하는 여과 알고리즘을 적용하여 월파의 발생 감지에 대한 정확성을 향상시켰다. AP50을 통해 월파 감지 결과의 정확도는 59.6%로 산정되었으며, 월파 감지 모델의 속도는 GPU 기준 70fps로 실시간 감지에 적합한 정확도와 속도를 보임을 확인하였다.

The purpose of this study was to propose technology to detect the wave in the image in real-time, and calculate the height of the wave-overtopping through image analysis using artificial intelligence. It was confirmed that the proposed wave overtopping detection system proposed in this study could detect the occurring of wave overtopping, even in severe weather and at night in real-time. In particular, a filtering algorithm for determining if the wave overtopping event was used, to improve the accuracy of detecting the occurrence of wave overtopping, based on a convolutional neural networks to catch the wave overtopping in CCTV images in real-time. As a result, the accuracy of the wave overtopping detection through AP50 was reviewed as 59.6%, and the speed of the overtaking detection model was 70fps based on GPU, confirming that accuracy and speed are suitable for real-time wave overtopping detection.

키워드

과제정보

이 논문은 2022년도 해양수산부 재원으로 해양수산과학기술진흥원의 지원을 받아 수행하는 "월파 정량 관측 기술 개발" (No.20220180, 해양 기후변화 진단 및 장기전망 연구) 및 기상청 한국기상산업기술원 연구개발사업 "CCTV를 활용한지진해일 자동 관측 기술 개발(KMI2021-01010)"의 지원을 받아 수행된 연구임을 밝힙니다.

참고문헌

  1. Bochkovskiy, A., Wang, C. and Liao, H.(2020), "YOLOv4: Optimal Speed and Accuracy of Object Detection", arXiv preprint arXiv:2004.10934.
  2. Chi, S., Zhang, C., Sui, T., Cao, Z., Zheng, J. and Fan, J.(2021). "Field observation of wave overtopping at sea dike using shore-based video images", Journal of Hydrodynamics, Vol. 33, pp. 57-672.
  3. Choi, E. H. and Kim, C. S.(2012), "A Design of Disaster Prevention System and Detection of Wave Overtopping Number for Storm Surge base on CCTV", Journal of Korea Multimedia Soiety, Vol. 15, No. 2, pp. 258-265. https://doi.org/10.9717/kmms.2012.15.2.258
  4. De Rouck J., Troch P., Van de Walle B., van Gent M., Van Damme L., De Ronde J., Frigaard P. and Murphy J.(2001), "Wave Run-up on Sloping Coastal Structures: Prototype Versus Scale Model Results", Proc. Int. Conf. on Coastlines, Structures and Breakwaters 2001, Institution of Civil Engineers, London, UK. 26-28 Sept. 2001.
  5. De Rouck, J. Verhaeghe, H. and Geeraerts. J.(2009), "Crest level assessment of coastal structures-General overview", Coastal Engineering, Vol. 56, pp. 99-210. https://doi.org/10.1016/j.coastaleng.2008.03.014
  6. Girshick, R., Donahue, J., Darrell, T. and Malik, J.(2014), "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation", IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580-587.
  7. Goda, Y., Kishiara, Y. and Kamiyama, Y.(1975), "Laboratory Investigation on the Overtopping Rates for Seawalls by Irregular Waves", Port and Harbour Research Institution, Vol. 14, pp. 3-44.
  8. Goda, Y.(2009), "Derivation of unified wave overtopping formulas for seawalls with smooth, impermeable surfaces based on selected CLASH datasets", Coastal Engineering, Vol. 56, pp. 385-399. https://doi.org/10.1016/j.coastaleng.2008.09.007
  9. Haridas, R. and Jyothi, R. L.(2019), "Convolutional Neural Networks: A Comprehensive Survey", International Journal of Applied Engineering Research, Vol. 14, pp. 780-789. https://doi.org/10.37622/IJAER/14.3.2019.780-789
  10. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P.(1998), "Gradient-Based Learning Applied to Document Recognition", Proceedings of the IEEE, Vol. 86, pp. 2278-2324. https://doi.org/10.1109/5.726791
  11. Lee, Y. H. and Kim, Y.(2020), "Comparison of CNN and YOLO for Object Detection", Journal of the Semiconductor & Display Technology, Vol. 19, No. 1, pp. 85-92.
  12. Liu, W., Anguelov, D., Erhan, D., Szegedy, S., Reed, S., Fu, C. and Berg, A.C.(2016), "SSD: Single Shot MultiBox Detector", European Conference on Computer Vision, Amsterdam, Netherlands, Oct. 8-16, pp. 21-37.
  13. Ministry of Oceans and Fisheris(MOF).(2020), Korea Design Standrard (KC Code KDS 64 10 10).
  14. MOIS, Ministry of the Interior and Safety. (2020). "Disaster Annual Report 2019", p. 431.
  15. Otsu, N.(1979), "A threshold selection method from gray-level histogram", IEEE Trans. on System Man Cybernetic, Vol. SMC-9, No. 1, pp. 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  16. Pullen, T., Allsop, N. W. H., Bruce, T., Kortenhaus, A., Schuttrumpf, H. and Van der Meer, J. W.(2007), EurOtop wave overtopping of sea defences and related structures: assessment manual.
  17. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A.(2016), "You Only Look Once: Unified, RealTime Object Detection. Computer Vision and Pattern Recognition (CVPR)", 2016 IEEE Conference, pp. 779-788.
  18. Redmon, J. and Farhadi, A.(2017), "YOLO9000: Better, Faster, Stronger", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271.
  19. Redmon, J. and Farhadi, A.(2018), "YOLOv3: An Incremental Improvement", arXiv preprint arXiv:1804.02767.
  20. Uijlings, J. R. R., Sande, K. E. A., Gevers, T. and Smeulders, A. W. M.(2013), "Selective Search for Object Recognition", International Journal of Computer Vision, Vol. 104, No. 2, pp. 154-171. https://doi.org/10.1007/s11263-013-0620-5
  21. Van der Meer, J. W., Allsop, N. W. H., Bruce, T., De Rouck, J. Kortenhaus, A. Pullen, T., Schuttrumpf, H., Troch, P. and Zanuttigh, B.(2018), Manual on Wave Overtopping of Sea Defenses and Related Structures-An Overtopping Manual Largely Based on European Research, But for Worldwide Application.
  22. Yoon, H. S., Park, J. H. and Jean, Y. H.(2017), "A Study on Wave Overtopping of the Seawall at Haeundae Marine City During the Passing of Typhoon Chaba", Journal of the Korean Society for Marine Environment and Energy, Vol. 20, No. 3, pp. 152-159. https://doi.org/10.7846/JKOSMEE.2017.20.3.152