한국지반공학회논문집 (Journal of the Korean Geotechnical Society)

  • 제39권7호
  • /
  • Pages.31-37
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  • 2023
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  • 1229-2427(pISSN)
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  • 2288-646X(eISSN)
한국지반공학회 (Korean Geotechnical Society)
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YOLO 알고리즘을 활용한 터널 GPR 이미지 내 강지보재 탐지

Detection of Steel Ribs in Tunnel GPR Images Based on YOLO Algorithm

  • 배병규 (부산대학교 토목공학과 ) ;
  • 안재훈 (부산대학교 사회환경시스템공학과) ;
  • 정현준 (국토안전관리원 디지털기획운영실 빅데이터전략팀 ) ;
  • 유창균 (국토안전관리원 시설안전관리단 터널실)
  • Bae, Byongkyu (Dept. of Civil Engrg., Pusan National Univ.) ;
  • Ahn, Jaehun (Dept. of Civil and Environmental Engrg., Pusan National Univ.) ;
  • Jung, Hyunjun (Bigdata Team, Dept. of DX, Korea Authority of Land & Infrastructure Safety (KALIS)) ;
  • Yoo, Chang Kyoon (Dept. of Tunnel, Korea Authority of Land & Infrastructure Safety (KALIS))
  • 투고 : 2023.05.31
  • 심사 : 2023.07.10
  • 발행 : 2023.07.31
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초록

터널은 지중에 건설되는 구조물이므로 육안으로 터널 강지보재의 위치 등의 확인이 불가능하다. 이에, 터널 유지관리시에는, 일반적으로 GPR 이미지를 활용하여 강지보재 탐지를 수행한다. 인공신경망을 통한 GPR 이미지 분석에 대한 연구는, 주로 지하배관, 도로 손상 등의 탐지에 집중되어 있으며, 강지보재 등의 터널 GPR 데이터를 분석한 사례는 해외와 국내 모두 제한적이다. 본 연구에서는, 합성곱 신경망을 기반으로 하는 1단계 객체인식 알고리즘인 YOLO를 활용하여, GPR 데이터를 바탕으로 한 터널 강지보재의 위치 탐지를 자동화하고, 그 성능을 분석한다. 원본 이미지 데이터는 총 512개이며 원본 이미지 데이터로 이루어진 데이터 세트와 원본 이미지 데이터와 증식기법이 적용된 이미지 데이터를 병합한 2,048개의 데이터로 이루어진 데이터 세트를 해석에 활용하였다. 증식한 데이터를 사용한 모델의 강지보재 누락율(전체 강지보재와 탐지하지 못한 지보재 숫자의 비율)은 0.38%, 원본 데이터만을 활용한 모델의 강지보재 누락율은 7.18%로 나타났다. 따라서, 분석 자동화 측면에서는, 증식기법이 적용된 데이터 세트를 활용하는 것이 더 실용적일 것으로 판단된다.

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

키워드

과제정보

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구(No. 2020R1A2C1012072)이며, 이에 깊은 감사를 드립니다.

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