인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
초록·키워드
In the shipbuilding industry, the safe transportation of heavy blocks is a crucial factor directly linked to productivity and has been highlighted for the last few decades. However, current operations heavily count on module Transporter(TP) operators' expertise and experiences, posing persistent safety risks due to limited visibility and operator fatigue. This study proposes a smart Health, Safety, and Environment(HSE) system based on visual intelligence to address these challenges, implements this system and demonstrates it. The proposed system integrates FHD cameras attached on all four sides of the TP, a You Only Look Once(YOLO)-based real-time object detection AI model, an edge computing-based image processing system, self-luminous Personal Protective Equipment(PPE), and LED guide beams for vehicle width monitoring. The system includes real-time processing for detecting five types of obstacles - people, vehicles, traffic lights, street lamps, and trees. The system incorporates optimized image processing algorithms for reliable performance while implementing QR code-based automatic location tracking, enhancing transport history management. During a field demonstration, the system significantly improved workers' visibility and consistently detected potential hazards during transport operations, substantially reducing accident rates. These results illustrate that smart technology can effectively enhance worker safety and operational efficiency, establishing a foundation for future smart shipyard implementations.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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