Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Performance Analysis of Detecting buried pipelines in GPR images using Faster R-CNN

Faster R-CNN을 활용한 GPR 영상에서의 지하배관 위치추적 성능분석

  • Ko, Hyoung-Yong (Department of Computer Science, Kyonggi University) ;
  • Kim, Nam-gi (Department of Computer Science, Kyonggi University)
  • 고형용 (경기대학교 컴퓨터과학과) ;
  • 김남기 (경기대학교 컴퓨터과학과)
  • Received : 2019.03.15
  • Accepted : 2019.05.20
  • Published : 2019.05.28
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Abstract

Various pipes are buried in the city as needed, such as water pipes, gas pipes and hydrogen pipes. As the time passes, buried pipes becomes aged due to crack, etc. these pipes has the risk of accidents such as explosion and leakage. To prevent the risks, many pipes are repaired or replaced, but the location of the pipes can also be changed. Failure to identify the location of the altered pipe may cause an accident by touching the pipe. In this paper, we propose a method to detect buried pipes by gathering the GPR images by using GPR and Learning with Faster R-CNN. Then experiments was carried out by raw data sets and data sets augmentation applied to increase the amount of images.

도심지에는 상 하수관로, 가스관, 수소관 등 필요에 따라 여러 가지 배관이 매설된다. 매설된 배관은 시간이 경과됨에 따라 균열 등으로 노후화되면서 폭발, 누수 등의 사고 발생 위험을 가지게 된다. 이러한 위험을 방지하기 위해 많은 노후 배관 수리, 교체되지만, 배관의 위치 또한 변경될 수 있다. 변경된 배관의 위치를 확인하지 못하면 배관을 건드려서 사고가 발생할 수 있다. 본 논문에서는 GPR을 사용하여 지하 단면 영상을 얻고, Faster R-CNN을 활용하여 지하 배관의 위치를 추정해보고, augmentation을 적용하여 부족한 데이터를 늘려서 실험을 진행하였다.

Keywords

JKOHBZ_2019_v9n5_21_f0001.png 이미지

Fig. 1. GPR Exploration path

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Fig. 2. GPR Sample Image

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Fig. 3. Faster R-CNN Process

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Fig. 4. Faster R-CNN 13-Layer Structure

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Fig. 5. Faster R-CNN Learning Process

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Fig. 6. Data set A

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Fig. 7. Data set B

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Fig. 8. Data set C

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Fig. 9. Data set A verification image example

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Fig. 10. Data set B verification image example

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Fig. 11. Data set C verification image example

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Fig. 12. Graph result with augmentation applied

Table 1. Experiment Environment

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Table 2. Experiment result symbol meaning

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Table 3. Experiment result

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Table 4. Result with augmentation applied

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