건설장비 객체인식 모델 성능 향상을 위한 생성형 AI 기반 합성 이미지 데이터 활용

• 영문명: Enhancing Construction Equipment Detection with Generative AI-Based Synthetic Image Data
• 발행기관: 한국BIM학회
• 저자명: 문성현(SeongHyun Moon) 이아름(Ahreum Lee) 이용주(Yong-Ju Lee) 박만우(Man-Woo Park)
• 간행물 정보: 『KIBIM Magazine』15권 3호, 77~86쪽, 전체 10쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2025.09.30

국문 초록

Dynamic and complex construction environments require effective monitoring to enhance productivity and ensure safety. While recent advancements in computer vision and deep learning have enabled the application of object detection models for this purpose, their performance is often constrained by the scarcity and diversity of training data. The process of collecting and annotating sufficient real-world construction images is labor-intensive and frequently limited by specific site conditions. This study addresses this challenge by investigating whether synthetic images, generated via a Text-to-Image model, can effectively supplement real datasets. For our experiments, we extracted roller and dozer classes from two publicly available datasets, ACID and MOCS, and generated corresponding synthetic images. We trained YOLOv11n models under three distinct scenarios: using synthetic images only, real images only, and a combination of both. Models trained with real data augmented by synthetic images showed consistent performance gains in both mAP50 and mAP50-95. This performance improvement was most significant with the smaller MOCS dataset, suggesting that synthetic data has a stronger supplementary effect when real-world data is limited.

목차

1. 서 론

2. 연구 방법

3. 실험 및 결과

4. 결 론

감사의 글

References

키워드

건설 현장 합성 이미지 객체 탐지 건설장비 생성형 AI Construction Site Synthetic Image Object Detection Construction Equipment Generative AI

참고문헌

• Applied Sciences
• Structural Health Monitoring
• Applied Sciences
• Automation in Construction
• Journal of Information Technology in Construction
• Iranian Journal of Science and Technology, Transactions of Civil Engineering
• Multimedia Tools and Applications
• European Conference on Computer Vision
• Automation in Construction
• Proceedings of the International Conference on Systems, Signals and Image Processing (IWSSIP)
• Construction and Building Materials
• In International Conference on Machine Learning
• Revista Alconpat
• Advances in Neural Information Processing Systems
• Sensors
• Journal of Big Data
• Automation in Construction
• Applied Sciences
• Engineering Structures
• Journal of Computing in Civil Engineering
• Journal of Construction Engineering and Management
• Automation in Construction