Circle detection is a fundamental problem in computer vision. However, conventional circle detection algorithms are usually time-consuming and sensitive to noise. In order to solve these shortcomings, we propose a fast circle detection algorithm based on information compression. First, we introduce the idea of information compression, which compresses the circular information on the image into a small number of points while removing some of the noise through sharpness estimation and orientation filtering. Then, the circle parameters stored in the information point are obtained by the average sampling algorithm with a time complexity of O(1) to obtain candidate circles. Finally, we set different constraints on the complete circle and the defective circle according to the sampling results and find the true circle from the candidate circles. The experimental results on the three datasets show that our method can compress the circular information in the image into 1% of the information points, and compared to RHT, RCD, Jiang, Wang and CACD, Precision, Recall, Time and F-measure are greatly improved.

Extracting circle information from images has always been a basic problem in computer vision. Common circle detection algorithms have some defects, such as poor noise resistance and slow computation speed. In this paper, we propose an anti-noise fast circle detection algorithm. In order to improve the anti-noise of the algorithm, we first perform curve thinning and connection on the image after edge extraction, then suppress noise interference by the irregularity of noise edges and extract circular arcs by directional filtering. In order to reduce the invalid fitting and speed up the running speed, we propose a circle fitting algorithm with five quadrants, and improve the efficiency of the algorithm by the idea of “divide and conquer”. We compare the algorithm with RCD, CACD, WANG and AS on two open datasets. The results show that we have the best performance under noise while keeping the speed of the algorithm.

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.