Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Deep Learning based Masonry Wall Defect Classification using a MEMS LiDAR

MEMS 라이다 센서를 활용한 심층학습 기반 조적벽체 결함 인식 기술

  • Hwang, Yeongseo (Dept. of Architecture and Civil Engineering, Chonnam National University) ;
  • Park, Gunhyung (Dept. of Architecture and Civil Engineering, Chonnam National University, Korea) ;
  • Yang, Kanghyeok (Dept. of Architecture and Civil Engineering & School of Architecture, Chonnam National University)
  • Received : 2022.09.23
  • Accepted : 2022.12.16
  • Published : 2023.01.30
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Abstract

Most of the maintenance and safety inspections of buildings are performed with visual assessment of the inspector, which consumes a lot of time and cost. With the development of computer vision and digital technologies such as 3D Laser scanners, automatic defect recognition using image processing and artificial intelligence has been widely studied. Current approach is largely relying on the image obtained from the camera and the recognition performance could be varied depending on the surrounding environment. Recently, studies using 3D Laser scanner are being conducted to solve these problems. However, terrestrial laser scanners are expensive, so it is difficult to apply at the construction site. Therefore, this study proposed a method that can recognize masonry wall defects using a Microelectromechanical systems based Light Detection and Ranging sensor that having much lower price and reliable performance. This study was performed using masonry wall structures and data were collected from samples having various types of defects in a laboratory environment. Masonry wall defects were recognized using ResNet-50 and VGG16 models, which are widely used in previous studies. As a result of the classification, ResNet-50 and VGG16 achieved 98.75% and 96.88% accuracy, respectively. The results of this study can be utilized in the development of real-time defect recognition method for a masonry wall at construction sites.

Keywords

Acknowledgement

이 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단 (No. 2021R1F1A1063338) 및 국토교통부/국토교통과학기술진흥원 (과제번호: 21CTAP-C163631-01)의 지원을 받아 수행된 연구임

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