한국지반신소재학회논문집 (Journal of the Korean Geosynthetics Society)

  • 제23권2호
  • /
  • Pages.43-52
  • /
  • 2024
  • /
  • 2508-2876(pISSN)
  • /
  • 2287-9528(eISSN)
한국지반신소재학회 (Korean Geosynthetics Society)
권호 표지
DOI QR코드

DOI QR Code

IoT 센서와 AI 카메라를 융합한 급경사지 상태 분석 시스템 개발

Development of a Slope Condition Analysis System using IoT Sensors and AI Camera

  • 이승주 ;
  • 정기연 ;
  • 이태훈 ;
  • 김영석
  • Seungjoo Lee (Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology) ;
  • Kiyen Jeong (R&D Team, Hanlim CE Corporation) ;
  • Taehoon Lee (R&D Team, Hanlim CE Corporation) ;
  • YoungSeok Kim (Northern Infrastructure Specialized Team, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2024.05.09
  • 심사 : 2024.05.31
  • 발행 : 2024.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 이상기후로 인한 급경사지 붕괴 위험이 증가되고 있으며, 급경사지 붕괴 위험의 사전 예측 및 경보 전파가 이루어지지 않아 인명과 재산 피해가 발생할 수 있다. 본 논문에서는 급경사지의 상태를 평가하기 위해 IoT 센서와 AI 기반 카메라를 융합한 급경사지 분석 시스템을 개발하였다. 시스템을 개발하기 위하여 급경사지 지반조건을 고려한 계측센서 하드웨어 및 펌웨어 설계, AI 기반 영상 분석 알고리즘 설계, 그리고 예·경보 솔루션 및 시스템 제작을 수행하였다. IoT 센서의 데이터와 AI 카메라 영상 분석을 통해 센서 데이터의 오차를 최소화하고, 데이터의 신뢰성을 향상시키고자 하였다. 또한 실제 급경사지에 적용하여 정확도(신뢰도)를 평가하였다. 그 결과, 센서 계측 오류는 0.1° 이내로 유지되었으며 계측 데이터의 전송률은 95%이상이었다. AI 기반의 영상 분석 시스템은 야간에도 부분 인식률 99%의 높은 성능을 나타내었다. 본 연구결과는 다양한 사회간접자본(SOC) 시설의 급경사지 상태 분석 및 스마트 유지관리 분야에도 적용할 수 있을 것으로 판단된다.

Recent abnormal climate conditions have increased the risk of slope collapses, which frequently result in significant loss of life and property due to the absence of early prediction and warning dissemination. In this paper, we develop a slope condition analysis system using IoT sensors and AI-based camera to assess the condition of slopes. To develop the system, we conducted hardware and firmware design for measurement sensors considering the ground conditions of slopes, designed AI-based image analysis algorithms, and developed prediction and warning solutions and systems. We aimed to minimize errors in sensor data through the integration of IoT sensor data and AI camera image analysis, ultimately enhancing the reliability of the data. Additionally, we evaluated the accuracy (reliability) by applying it to actual slopes. As a result, sensor measurement errors were maintained within 0.1°, and the data transmission rate exceeded 95%. Moreover, the AI-based image analysis system demonstrated nighttime partial recognition rates of over 99%, indicating excellent performance even in low-light conditions. Through this research, it is anticipated that the analysis of slope conditions and smart maintenance management in various fields of Social Overhead Capital (SOC) facilities can be applied.

키워드

과제정보

This research was supported by the 'Smart Construction Technology Verification Research Support Project(20240051-006)' of the Korea Institute of Civil Engineering and Building Technology.

참고문헌

  1. Augustin, A., Yi, J., Clausen, T. and Townsley, W. M. (2016), "A Study of LoRa: Long Range & Low Power Networks for the Internet of Things", Sensors, Vol.16, No.9, p.1466.
  2. Bradski, G. and Kaehler, A. (2000), OpenCV, Dr. Dobb's Journal Of Software Tools, Vol.3, No.2.
  3. Jin, J. H., Lim, H. T., Bibek, T., Chang, S. H. and Kim, Y. S. (2018), "Slope Behavior Analysis using the Measurement of GFRP Underground Displacement", Journal of the Korean Geosynthetics Society, Vol.17, No.4, pp.11-19. (in Korean)
  4. Jo, J. H. and Jin, J. H. (2017), "Redundant MEMS IMU Sensors and Fault Detection of Sensors", 2017 SASE Spring Conference, pp.33-35. (in Korean)
  5. Kang, H. S., Suk, J. W. and Kim, H. J. (2019), "Analysis on Characteristics of Volumetric Water Content through LargeScale Slope Failure Experiment", Proc. of the Korean GeoEnvironmental Society Conference, Vol.2019, No.9, pp.31-32. (in Korean)
  6. Kim, H. G. (2017), Study on Field Investigation and Maintenance of Dangerous Slopes in National Parks, Ph.D. Thesis, Pusan National University.
  7. Kirillov, A. (2013), "Aforge.net Framework", Retrieved September 25th from http://www.aforgenet.com, Vol.68, pp.47-52.
  8. Koo, S., Lim, E. T., Jung, Y. H., Suk, J. W. and Kim, S. S. (2023), "Study on Applicability of Cloth Simulation Filtering Algorithm for Segmentation of Ground Points from Drone LiDAR Point Clouds in Mountainous Areas", Korean Journal of Remote Sensing, Vol.39, No.5-2, pp.827-835. (in Korean)
  9. Korea Forest Service (2024), Status of Landslide Damage and Recovery Costs, e-National Index, https://www.index.go.kr/unity/potal/main/EachDtlPageDetail.do?idx_cd=1311. (in Korean)
  10. Korea Meteorological Administration (2020), Korean Climate Change Assessment Report 2020, Assessment Report. (in Korean).
  11. Kungcharoen, K., Palangsantikul, P. and Premchaiswadi, W. (2012), "Development of Object Detection Software for a Mobile Robot using an AForce. Net framework.", 2011 Ninth International Conference on ICT and Knowledge Engineering, IEEE, pp.201-206.
  12. Lee, S. M. and Yoon, H. M. (2022), Measures to Improve Landslide Management in Seoul in Response to Climate Change, Report, The Seoul Institute, pp.1-147. (in Korean)
  13. Ministry of Public Safety and Security (2016), Steep Slope Management Manual, p.18. (in Korean)
  14. Ministry of the Interior and Safety (2023), Analysis of the Causes of Earth and Sand Disasters and Measures to Prevent Human Damage, Press Release. (in Korean)
  15. Oh, Y. K. (2023), "Analysis of Disaster Management Issues due to Concentrated Heavy Rain in 2023: Focusing on the Field Situation Reporting System", Korea Institute of Public Administration Issue Paper, No.132. p.2. (in Korean)
  16. Park, J. K., Lee, S. Y., Yang, I. T. and Kim, D. M. (2010), "Monitoring of the Natural Terrain Behavior Using the Terrestrial LiDAR", KSCE Journal of Civil and Environmental Engineering Research, Vol.30, No.2D, pp.191-198. (in Korean).