  Object Detection (OD) has proven to be a significant computer vision method
in extracting localized class information and has multiple applications in the
industry. Although many of the state-of-the-art (SOTA) OD models perform well
on medium and large sized objects, they seem to under perform on small objects.
In most of the industrial use cases, it is difficult to collect and annotate
data for small objects, as it is time-consuming and prone to human errors.
Additionally, those datasets are likely to be unbalanced and often result in an
inefficient model convergence. To tackle this challenge, this study presents a
novel approach that injects additional data points to improve the performance
of the OD models. Using synthetic data generation, the difficulties in data
collection and annotations for small object data points can be minimized and to
create a dataset with balanced distribution. This paper discusses the effects
of a simple proportional class-balancing technique, to enable better anchor
matching of the OD models. A comparison was carried out on the performances of
the SOTA OD models: YOLOv5, YOLOv7 and SSD, for combinations of real and
synthetic datasets within an industrial use case.
