A Fast Circle Detection Algorithm Based on Circular Arc Feature Screening

Circle detection is a crucial problem in computer vision and pattern recognition. In this paper, we propose a fast circle detection algorithm based on circular arc feature screening. In order to solve the invalid sampling and time consumption of the traditional circle detection algorithms, we improve the fuzzy inference edge detection algorithm by adding main contour edge screening, edge refinement, and arc-like determination to enhance edge positioning accuracy and remove unnecessary contour edges. Then, we strengthen the arc features with step-wise sampling on two feature matrices and set auxiliary points for defective circles. Finally, we built a square verification support region to further find the true circle with the complete circle and defective circle constraints. Extensive experiments were conducted on complex images, including defective, blurred-edge, and interfering images from four diverse datasets (three publicly available and one we built). The experimental results show that our method can remove up to 89.03% of invalid edge points by arc feature filtering and is superior to RHT, RCD, Jiang, Wang, and CACD in terms of speed, accuracy, and robustness.