Synthetic Data Generation for Scarce Road Scene Detection Scenarios

Recent advancements in generative models have led to significant improvements in the quality of generated images, making them virtually indistinguishable from real ones. However, using AI generated images for training robust computer vision models for real-world applications, especially object detection in road scene perception, is still a challenge. AI generated images usually lack the required diversity and scene complexity where specific objects appear with critically low frequency in the available real datasets. An example of such applications is the detection of emergency vehicles like police cars, fire trucks, and ambulances in road scenes. These vehicles appear with drastically low frequencies in available datasets. Successfully generating synthetic images of road scenes that include these types of vehicles and using them in training downstream models would prove useful for autonomous driving vehicles, mitigating safety concerns on the road. To address this, this paper proposes a new approach for synthetically generating diverse, complex, and domain-compatible images of emergency vehicles in road scenes by employing a diffusion-based generative model pretrained on a generic dataset. We investigate the impact of using generated synthetic images in the performance of downstream object detection models. Finally, we thoroughly discuss challenges of generating synthetic datasets with the proposed approach.