Circle detection in digital images is an important problem in computer vision, pattern recognition, and artificial intelligence. However, the common circle detection strategies, including random sample consensus, randomized Hough transform, and randomized circle detection, have a very low sampling efficiency and thus a slow detection speed, owing to aimless random sampling. This paper proposes a fast and accurate randomized circle detection algorithm, with the aim to improve the speed and accuracy of circle detection based on random sampling. The proposed algorithm mainly focuses on four aspects: calculating circle parameters, determining candidate circles, searching for true circle, and improving detection accuracy. To verify the effectiveness of our algorithm, contrastive experiments were conducted on lots of synthetic and real images. The results show that our algorithm achieved much higher detection speed and accuracy than random sample consensus, randomized Hough transform, and randomized circle detection, and realized similar robustness as the three contrastive strategies. The research ideas can also be applied to ellipse detection.

数字图像中的圆圈检测是计算机视觉，模式识别和人工智能中的重要问题。然而，由于无目标的随机采样，包括随机样本共识，随机霍夫变换和随机圆检测在内的常见圆检测策略具有非常低的采样效率，因此检测速度较慢。提出一种快速，准确的随机圆检测算法，以提高基于随机采样的圆检测的速度和准确性。所提出的算法主要集中在四个方面：计算圆参数，确定候选圆，搜索真实圆以及提高检测精度。为了验证我们算法的有效性，在大量合成图像和真实图像上进行了对比实验。结果表明，与随机样本共识，随机Hough变换和随机圆检测相比，我们的算法具有更高的检测速度和准确性，并且实现了与三种对比策略相似的鲁棒性。研究思路也可以应用于椭圆检测。