Journal of the Architectural Institute of Korea Planning & Design (대한건축학회논문집:계획계)

Architectural Institute of Korea (대한건축학회)
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A Development on Deep Learning-based Detecting Technology of Rebar Placement for Improving Building Supervision Efficiency

감리업무 효율성 향상을 위한 딥러닝 기반 철근배근 디텍팅 기술 개발

  • Received : 2020.03.20
  • Accepted : 2020.05.13
  • Published : 2020.05.30
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Abstract

The purpose of this study is to suggest a supervisory way to improve the efficiency of Building Supervision using Deep Learning, especially object detecting technology. Since the establishment of the Building Supervision system in Korea, it has been changed and improved many times systematically, but it is hard to find any improvement in terms of implementing methods. Therefore, the Supervision is until now the area where a lot of money, time and manpower are needed. This might give a room for superficial, formal and documentary supervision that could lead to faulty construction. This study suggests a way of Building Supervision which is more automatic and effective so that it can lead to save the time, effort and money. And the way is to detect the hoop-bars of a column and count the number of it automatically. For this study, we made a hoop-bar detecting network by transfor learnning of YOLOv2 network through MATLAB. Among many training experiments, relatively most accurate network was selected, and this network was able to detect rebar placement in building site pictures with the accuracy of 92.85% for similar images to those used in trainings, and 90% or more for new images at specific distance. It was also able to count the number of hoop-bars. The result showed the possibility of automatic Building Supervision and its efficiency improvement.

Keywords

Acknowledgement

이 연구는 2020년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 이공분야기초연구사업임. 과제번호 : 2019R1A2C2006983

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