Earticle

초록

영어

Quality control is difficult to secure objectivity because the quality management of reinforced concrete construction is made by the subjective judgment by experts through a checklist. This study aims to establish an automation process for quality management of reinforced concrete frameworks through a Deep Learning algorithm based on object recognition. Through this, it is possible to save time more objectively than before, and the purpose is to provide intuitive judgment through visualization. This study proposed a quality control process through the learning and verification process with the image data set obtained from AI Hub, and mAP was derived with an accuracy of 0.687. The drone image data of the actual site was determined using the derived algorithm. 3D modeling is performed through the determined drone image to ensure the safety of the inspector and intuitive judgment. The proposed process cannot be confirmed the determined line when matched with a 3D model using PIX4D, but it is judged that it will be applicable to additional processes through the replacement of modeling programs and improvement of Deep-Learning algorithms.

목차

Abstract
1. Introduction
2. Related Work
3. Build a Dataset for Deep-Learning based on Object Recognition
3.1 Deep-Learning based on object recognition
3.2 Formed Dataset
4. Result of Predicting Deep-Learning based on Object Recognition
5. Case Study
5.1 Determining the Image of Validation Dataset
5.2 Determining the Image of Drone on Site
6. Conclusion
REFERENCES

저자

• 강은아 [ Kang, Eun-Ah | 영남대학교 건축학과 건축공학전공 석사과정 ]
• 김상용 [ Kim, Sangyong | 영남대학교 건축학부 부교수, 공학박사 ]
• 김승호 [ Kim, Seungho | 영남이공대학교 건축과 조교수, 공학박사 ] Corresponding Author

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