지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.1
- 수록면
- 313 - 320 (8page)
이용수
초록· 키워드
오류제보하기
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.
목차
Abstract
1. 서론
2. 이론적 고찰
3. 조적벽체 결함 인식 모델 개발 및 실험
4. 조적벽체 결함인식 기술 성능 분석
5. 결론
REFERENCES
참고문헌 (25)
참고문헌 신청
Brackenbury, D., Brilakis, I., & DeJong, M. (2019). Automated defect detection for masonry arch bridges. In International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving data-informed decision-making, 3-9.
Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361-378.
Chaiyasarn, K., Sharma, M., Ali, L., Khan, W., & Poovarodom, N. (2018). Crack detection in historical structures based on convolutional neural network. GEOMATE Journal, 15(51), 240-251.
Dais, D., Bal, I. E., Smyrou, E., & Sarhosis, V. (2021). Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction, 125, 103606.
Hallee, M. J., Napolitano, R. K., Reinhart, W. F., & Glisic, B. (2021). Crack detection in images of masonry using cnns. Sensors, 21(14), 4929.
Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361-378.
Chaiyasarn, K., Sharma, M., Ali, L., Khan, W., & Poovarodom, N. (2018). Crack detection in historical structures based on convolutional neural network. GEOMATE Journal, 15(51), 240-251.
Dais, D., Bal, I. E., Smyrou, E., & Sarhosis, V. (2021). Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction, 125, 103606.
Hallee, M. J., Napolitano, R. K., Reinhart, W. F., & Glisic, B. (2021). Crack detection in images of masonry using cnns. Sensors, 21(14), 4929.
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