Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

Abstract
1. 서론
2. 정사사진을 이용한 균열 탐지 기법
    2.1 정사사진 취득
    2.2 머신러닝 기반 균열 탐지 기법
3. 균열 탐지 기법 평가 실험
    3.1 실험 계획
    3.2 실험 결과 및 분석
4. 결론
References

• 장아름(고려대학교 건축사회환경공학과) | Jang Arum (School of Civil, Environmental, and Architectural Engineering, Korea University)
• 정상기(고려대학교 건축사회환경공학과) | Jeong Sanggi (School of Civil, Environmental, and Architectural Engineering, Korea University)
• 박진한(현대엔지니어링(주) 스마트기술센터 스마트 컨스트럭션실) | Park Jinhan (Smart Construction team, Smart Technology Center, Hyundai Engineering Co., Ltd.)
• 강창훈(현대엔지니어링(주) 스마트기술센터 스마트 컨스트럭션실) | Kang Chang-hoon (Smart Construction team, Smart Technology Center, Hyundai Engineering Co., Ltd.)
• 주영규(고려대학교 건축사회환경공학부 교수) | Ju Young K (School of Civil, Environmental, and Architectural Engineering, Korea University) Corresponding author