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2024 Vol.23, Issue 2 Preview Page

Research Article

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

IoT 센서와 AI 카메라를 융합한 급경사지 상태 분석 시스템 개발

Seungjoo Lee1
,
Kiyen Jeong2
,
Taehoon Lee3
,
YoungSeok Kim4*
이 승주1
,
정 기연2
,
이 태훈3
,
김 영석4*
1Member, Researcher, Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-Daero, Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Republic of Korea
2Non-Member, Managing Director, R&D Team, Hanlim CE Corporation., 201, 138 Huimang-ro, Suseong-gu, Daegu 42151, Republic of Korea
3Non-Member, CEO, Hanlim CE Corporation., 201, 138 Huimang-ro, Suseong-gu, Daegu 42151, Republic of Korea
4Member, Senior Research Fellow, Northern Infrastructure Specialized Team, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-Daero, Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Republic of Korea
*Corresponding Author
30 June 2024. pp. 43-52
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Abstract

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.

Keywords
IoT sensor
AI camera
Video analysis algorithms
Slope monitoring system
Field test

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.

References
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.

10.3390/s1609146627618064PMC5038744
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 Large- Scale Slope Failure Experiment", Proc. of the Korean Geo- Environmental 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.

10.1109/ICTKE.2012.6152407
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).

Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 23
  • No :2
  • Pages :43-52
  • Received Date : 2024-05-09
  • Revised Date : 2024-05-28
  • Accepted Date : 2024-05-31
  • DOI :https://doi.org/10.12814/jkgss.2024.23.2.043
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