  Deep object detection models have achieved notable successes in recent years,
but one major obstacle remains: the requirement for a large amount of training
data. Obtaining such data is a tedious process and is mainly time consuming,
leading to the exploration of new research avenues like synthetic data
generation techniques. In this study, we explore the usability of Stable
Diffusion 2.1-base for generating synthetic datasets of apple trees for object
detection and compare it to a baseline model trained on real-world data. After
creating a dataset of realistic apple trees with prompt engineering and
utilizing a previously trained Stable Diffusion model, the custom dataset was
annotated and evaluated by training a YOLOv5m object detection model to predict
apples in a real-world apple detection dataset. YOLOv5m was chosen for its
rapid inference time and minimal hardware demands. Results demonstrate that the
model trained on generated data is slightly underperforming compared to a
baseline model trained on real-world images when evaluated on a set of
real-world images. However, these findings remain highly promising, as the
average precision difference is only 0.09 and 0.06, respectively. Qualitative
results indicate that the model can accurately predict the location of apples,
except in cases of heavy shading. These findings illustrate the potential of
synthetic data generation techniques as a viable alternative to the collection
of extensive training data for object detection models.
