A Fast Circle Detector with Efficient Arc Extraction
Yang Liu, Honggui Deng, Zeyu Zhang, Qiguo Xu
Abstract
Circle detection is a crucial problem in computer vision and pattern recognition. Improving the accuracy and efficiency of circle detectors has important scientific significance and excellent application value. In this paper, we propose a circle detection method with efficient arc extraction. In order to reduce edge redundancy and eliminate crossing points, we present an edge refinement algorithm to refine the edges into single-pixel-wide branchless contour curves. To address the contour curve segmentation difficulty, we improved the CTAR (Chord to Triangular Arms Ratio) corner detection method to enhance corner point detection and segment the contour curves based on corner points. Then, we used the relative position constraint of arcs to improve the circle detection accuracy further. Finally, we verified the feasibility and reliability of the proposed method by comparing our approach with five other methods using three datasets. The experimental results showed that the presented method had the advantages of anti-obscuration, anti-defect, and real-time performance over other methods.