Generating Pseudo Label of Object Detector for Construction Site Monitoring

The performance of deep learning models can be significantly degraded on unseen data that has different visual characteristics compared to a domain where training data was collected. A simple and obvious way to maintain the performance of deep learning models is to prepare training data again in a new domain where target objects and backgrounds have different appearances compared to the original. However, it is not a trivial task considering time and efforts required in data preparation. To address this issue, this study proposes a pseudo label generation method from images that can automatically collect video clips for objects of interest and assign labels. The proposed method consists of a moving object detector to extract target objects in images and a classifier to assign labels on the extracted regions. The findings of this study provide important knowledge for construction site monitoring in securing the performance of computer vision models in various environments.