Development of training image database using web crawling for vision-based site monitoring.

As most of the state-of-the-art technologies for vision-based monitoring were originated from machine learning or deep learning algorithms, it is crucial to build a large and rich training image database (DB). For this purpose, this paper proposes an automated framework that builds a large, high-quality training DB for construction site monitoring. The framework consists of three main processes: (1) automated construction image collection using web crawling, (2) automated image labeling using an image segmentation model, and (3) fully randomized foreground-background cross-oversampling. Using the developed framework, it was possible to automatically construct a training DB, composed of 5864 images, for the detection of construction objects in 53.5 min. The deep learning model trained by the DB successfully detected construction resources with an average precision of 92.71% and a recall rate of 88.14%. The findings of this study can reduce the time and effort required to develop vision-based site monitoring technologies. • Automatically developed training image DB using web crawling for construction monitoring • Automatically labeled web crawled images using a semantic segmentation model • Foreground-background cross-oversampling to synthesize photorealistic site images • Validated acceptable performance with 92.7% precision and 88.1% recall rate [ABSTRACT FROM AUTHOR]