이동식 터널 스캐닝 시스템의 영상 이미지 왜곡에 대한 현장 성능 평가
Field evaluation of image distortion in mobile tunnel scanning system
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초록
본 연구에서는 터널 자동화 점검 시 영상 이미지의 정합 정밀도와 왜곡의 영향에 중점을두고 이동식 터널 스캐닝 시스템의 성능을 평가하는 방안을 제안하였다. 도로 터널의 콘크리트 라이닝에 마커를 부착한 상태에서 80 km/h의 속도로 촬영한 영상 이미지의 왜곡을 평가하기 위해 현장 실험을 수행하였다. 수동으로 촬영한 참조 이미지에서 마커 위치에 대한 RMSE (root meansquare error)는 9.78 mm로 나타났으며, 수평 거리 RMSE는 0.13%, 수직 거리 RMSE는 0.11%의 낮은 왜곡 성능을 보였다. 그러나 80 km/h의 속도로 촬영한 영상에서는 수평 거리 RMSE가 41.17%, 수직 거리 RMSE가 34.38%로 심한 왜곡이 발생한 것으로 나타났다. 이러한 이미지의 왜곡은 고속 촬영과 터널 곡률에 대한 부적절한 보정으로 기인된 것으로추정된다. 제안된 현장 성능 평가 방법은 객관적인 성능 평가뿐만 아니라, 이동식 터널 스캐닝 시스템의 정밀도를 확보하기 위한 개선 사항들을 파악하는 것에도 큰 도움이 될 것으로 기대된다.
Abstract
In this study, we proposed a method to evaluate the performance of a mobile tunnelscanning system, focusing on the impact of registration precision and distortion ofvideo images during automated tunnel inspection. A field experiment was conductedto evaluate the distortion of video images taken at a speed of 80 km/h with markersattached to the concrete lining of a road tunnel. In the manually captured referenceimage, the root mean square error (RMSE) for the marker position was found to be9.78 mm, and the horizontal distance RMSE was 0.13%, and the vertical distanceRMSE showed low distortion performance of 0.11%. However, in the image taken ata speed of 80 km/h, severe distortion occurred, with the horizontal distance RMSEbeing 41.17% and the vertical distance RMSE being 34.38%. The distortion of theseimages is believed to be caused by high-speed shooting and inadequate correction fortunnel curvature. The proposed on-site performance evaluation method is expected tobe of great help not only in objective performance evaluation but also in identifyingimprovements to ensure the precision of mobile tunnel scanning systems.
참고문헌 (23)
- Bae, S. J., "Performance evaluation method of tunnel scanner for lining crack detection", Journal of the Society of Disaster Information, 17(1), pp.39~52 (2021)
- "CNT 887, Construction New Technology" (2020)[URL]
- "CNT 898, Construction New Technology" (2020)[URL]
- "CNT 935, Construction New Technology" (2022)[URL]
- Gong, Q., "Automatic subway tunnel crack detection system based on line scan camera", Structural Control and Health Monitoring, 28(8), pp.e2776~ (2021)
- "GT-8K" (2022)[URL]
- Huang, H., "Inspection equipment study for subway tunnel defects by grey-scale image processing", Advanced Engineering Informatics, 32, pp.188~201 (2017)
- Jiang, Y., "Quantitative condition inspection and assessment of tunnel lining", Automation in Construction, 102, pp.258~269 (2019)
- Lee, C., "Performance analysis of tunnel scanning system based on Japanese performance evaluation system", Journal of Korean Tunnelling and Underground Space Association, 25(4), pp.331~344 (2023)
- Lee, C., "Non-contact mobile inspection system for tunnels : a review", Journal of Korean Tunnelling and Underground Space Association, 25(3), pp.245~259 (2023)
- Lee, S. Y., "Development of an inspection system for cracks in a concrete tunnel lining", Canadian Journal of Civil Engineering, 34(8), pp.966~975 (2007)
- MLIT, "Inspection support technology performance catalog", Ministry of Land, Infrastructure, Transport and Tourism, ~ (2023)
- "MMSD" (2018)[URL]
- Qin, S., "Rapid and automatic image acquisition system for structural surface defects of high-speed rail tunnels", KSCE Journal of Civil Engineering, 28(2), pp.967~989 (2023)
- Sasama, H., "Inspection system for railway facilities using a continuously scanned image", Electrical Engineering in Japan, 125(2), pp.52~64 (1998)
- "Tunnel Catcher 3" (2019)[URL]
- "Tunnel Tracer" (2018)[URL]
- Ukai, M., "Advanced inspection system of tunnel wall deformation using image processing", Quarterly Report of RTRI, 48(2), pp.94~98 (2007)
- Ukai, M., "Development of inspection system of railway facilities using continuous scan image", WIT Transactions on The Built Environment, 20, pp.61~70 (1996)
- Yasuda, T., "Smart tunnel inspection and assessment using mobile inspection vehicle, non-contact radar and AI", Proceedings of the 37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use To New Stage of Construction Robot, ISARC 2020, pp.1373~1379 (2020)
- Yasuda, T., "Tunnel inspection system by using high-speed mobile 3D survey vehicle : MIMM-R", Journal of the Robotics Society of Japan, 34(9), pp.589~590 (2016)
- Yu, S. N., "Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel", Automation in Construction, 16(3), pp.255~261 (2007)
- Zhang, W., "Automatic crack detection and classification method for subway tunnel safety monitoring", Sensors, 14(10), pp.19307~19328 (2014)