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，可提取局部类别信息并在工业领域具有多种应用。尽管许多最先进的（SOTA）目标检测模型在中大型目标上表现良好，但它们在小型目标上表现欠佳。在大多数工业用例中，收集和标注小目标数据既耗时又易引入人为错误，此外这些数据集通常存在类别不平衡问题，导致模型收敛效率低下。为应对这一挑战，本研究提出一种新方法，通过注入额外数据点来提升目标检测模型性能。利用合成数据生成技术，可最小化小目标数据收集与标注的难度，并构建分布均衡的数据集。本文探讨了简单比例类别平衡技术对优化目标检测模型锚点匹配的效果，并在工业用例中比较了YOLOv5、YOLOv7和SSD三种SOTA目标检测模型在真实数据集与合成数据集组合上的性能表现。