Synthetic Datasets for Rebar Instance Segmentation Using Mask R-CNN

Abstract

The construction and inspection of reinforcement rebar currently rely entirely on manual work, which leads to problems such as high labor requirements and labor costs. Rebar image detection using deep learning algorithms can be employed in construction quality inspection and intelligent construction; it can check the number, spacing, and diameter of rebar on a construction site, and guide robots to complete rebar tying. However, the application of deep learning algorithms relies on a large number of datasets to train models, while manual data collection and annotation are time-consuming and laborious. In contrast, using synthetic datasets can achieve a high degree of automation of annotation. In this study, using rebar as an example, we proposed a mask annotation methodology based on BIM software and rendering software, which can establish a large and diverse training set for instance segmentation, without manual labeling. The Mask R-CNN trained using both real and synthetic datasets demonstrated a better performance than the models trained using only real datasets or synthetic datasets. This synthetic dataset generation method could be widely used for various image segmentation tasks and provides a reference for other computer vision engineering tasks and deep learning tasks in related fields.