Development of a virtual environment for rapid generation of synthetic training images for artificial intelligence object recognition

In the field of machine learning and computer vision, the lack of annotated datasets is a major challenge for model development and accuracy improvement. Synthetic data generation addresses this issue by providing large, diverse, and accurately annotated datasets, thereby enhancing model training and validation. This study presents a Unity-based virtual environment that utilises the Unity Perception package to generate high-quality datasets. First, high-precision 3D (Three-Dimensional) models are created using a 3D structured light scanner, with textures processed to remove specular reflections. These models are then imported into Unity to generate diverse and accurately annotated synthetic datasets. The experimental results indicate that object recognition models trained with synthetic data achieve a high rate of performance on real images, validating the effectiveness of synthetic data in improving model generalisation and application performance. Monocular distance measurement verification shows that the synthetic data closely matches real-world physical scales, confirming its visual realism and physical accuracy.