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.

翻译：目标检测（OD）已被证明是一种重要的计算机视觉方法，能够提取局部类别信息，并在工业中拥有多种应用。尽管许多最先进的（SOTA）OD模型在中大型目标上表现良好，但在小目标上却性能不足。在大多数工业用例中，收集和标注小目标数据既耗时又容易出错，因此存在困难。此外，这些数据集往往不平衡，常常导致模型收敛效率低下。为解决这一挑战，本研究提出了一种新颖的方法，通过注入额外数据点来提升OD模型的性能。利用合成数据生成，可以最小化小目标数据点收集和标注的困难，并创建具有平衡分布的数据集。本文讨论了简单的比例类平衡技术，以使OD模型实现更好的锚点匹配。针对YOLOv5、YOLOv7和SSD等SOTA OD模型，在工业用例中结合真实与合成数据集进行了性能比较。