이미지 기반 실시간 건설 현장 장비 및 작업자 모니터링을 위한 딥러닝 플랫폼 아키텍처 도출

• 영문명: Deep learning platform architecture for monitoring imagebased real-time construction site equipment and worker
• 발행기관: 한국BIM학회
• 저자명: 강태욱(Kang, Tae-Wook) 김병곤(Kim, Byung-Kon) 정유석(Jung, Yoo-Seok)
• 간행물 정보: 『KIBIM Magazine』11권 2호, 24~32쪽, 전체 9쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2021.06.30

영문 초록

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

목차

1. 서론

2. 실시간 객체인식 딥러닝기술 이론고찰

3. 실시간 건설 현장 객체 탐지 요구사항 및 프로세스 정의

4. 프로토타입 개발 및 고려사항 도출

5. 결론

감사의 글

References

키워드

딥러닝 공사 현장 관리 플랫폼 아키텍처 고려사항 Deep Learning Construction Site Management Platform Architecture Consideration