| Title |
Solitary Work Detection of Heavy Equipment Using Computer Vision |
| Authors |
정인수(Jeong, Insoo) ; 김진우(Kim, Jinwoo) ; 지석호(Chi, Seokho) ; 노명일(Roh, Myungil) ; (Biggs, Herbert) |
| DOI |
https://doi.org/10.12652/Ksce.2021.41.4.0441 |
| Keywords |
건설현장; 중장비; 신호수; 단독작업; 상호작용; 컴퓨터비전 Construction site; Heavy equipment; Spotter; Solitary work; Interaction; Computer vision |
| Abstract |
Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision
technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management. |