Domain Randomization for Object Detection: A Survey

Object detection plays a critical role in various real-world applications, ranging from autonomous driving to industrial automation and medical imaging. However, models trained on synthetic data often struggle to generalize due to the domain gap between simulated environments and real-world scenarios. Domain randomization has emerged as a promising solution to bridge this gap by introducing controlled variability in synthetic datasets, enabling models to learn invariant and robust features. This paper provides a comprehensive survey of domain randomization techniques, exploring their theoretical foundations, applications, and methodologies for improving object detection models. We examine key randomization parameters such as appearance, spatial, environmental, and physical property variations, highlighting their impact on generalization. Furthermore, we discuss challenges associated with domain randomization, including overfitting to synthetic randomness and computational constraints. Finally, we outline future directions for integrating domain randomization with other learning paradigms, such as generative adversarial networks and self-supervised learning, to further enhance adaptability and performance in real-world object detection tasks.