| Title |
Automated Training Database Development through Image Web Crawling for Construction Site Monitoring |
| Authors |
황정빈(Hwang, Jeongbin) ; 김진우(Kim, Jinwoo) ; 지석호(Chi, Seokho) ; 서준오(Seo, JoonOh) |
| DOI |
https://doi.org/10.12652/Ksce.2019.39.6.0887 |
| Keywords |
건설현장;영상 분석;웹 크롤링;학습 데이터베이스;건설공종;건설장비 Construction sites;Vision-based analytics;Web crawling;Training database;Construction work type;Construction equipment |
| Abstract |
Many researchers have developed a series of vision-based technologies to monitor construction sites automatically. To achieve high performance of vision-based technologies, it is essential to build a large amount and high quality of training image database (DB). To do that, researchers usually visit construction sites, install cameras at the jobsites, and collect images for training DB. However, such human and site-dependent approach requires a huge amount of time and costs, and it would be difficult to represent a range of characteristics of different construction sites and resources. To address these problems, this paper proposes a framework that automatically constructs a training image DB using web crawling techniques. For the validation, the authors conducted two different experiments with the automatically generated DB: construction work type classification and equipment classification. The results showed that the method could successfully build the training image DB for the two classification problems, and the findings of this study can be used to reduce the time and efforts for developing a vision-based technology on construction sites. |