과제정보
본 논문은 2021년 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임을 밝히며 이에 감사를 드립니다. (No. 2020R1A2B5B01001609)
The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.
본 논문은 2021년 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임을 밝히며 이에 감사를 드립니다. (No. 2020R1A2B5B01001609)