  Despite the notable accomplishments of deep object detection models, a major
challenge that persists is the requirement for extensive amounts of training
data. The process of procuring such real-world data is a laborious undertaking,
which has prompted researchers to explore new avenues of research, such as
synthetic data generation techniques. This study presents a framework for the
generation of synthetic datasets by fine-tuning pretrained stable diffusion
models. The synthetic datasets are then manually annotated and employed for
training various object detection models. These detectors are evaluated on a
real-world test set of 331 images and compared against a baseline model that
was trained on real-world images. The results of this study reveal that the
object detection models trained on synthetic data perform similarly to the
baseline model. In the context of apple detection in orchards, the average
precision deviation with the baseline ranges from 0.09 to 0.12. This study
illustrates the potential of synthetic data generation techniques as a viable
alternative to the collection of extensive training data for the training of
deep models.
