Leveraging synthetic data for zero–shot and few–shot circle detection in real–world domains

Abstract

Circle detection plays a pivotal role in computer vision, underpinning applications from industrial inspection and bioinformatics to autonomous driving. Traditional methods, however, often struggle with real–world complexities, as they demand extensive parameter tuning and adaptation across different domains. In this paper, we present the Synthetic Circle Dataset (SynCircle), a large synthetic image dataset designed to train a YOLO v10 network for circle detection. The YOLO v10 network, pre–trained solely on synthetic data, demonstrates remarkable off–the–shelf performance that surpasses conventional methods in various practical scenarios. Furthermore, we show that incorporating just a few labeled real images for fine–tuning can significantly boost performance, reducing the need for large annotated datasets. To promote reproducibility and streamline adoption, we publicly release both the trained YOLO v10 weights and the full SynCircle dataset.