Abstract:

In view of the problem that it is difficult to ensure high efficiency and high accuracy in circle fitting algorithm in the same time, a circle fitting algorithm based on multi-level optimization is proposed in this study. The 3σ criterion is used to remove the coarse error points, and the random sampling consistency is improved by reducing the randomness of subset selection, the descending operation of the circle model and the threshold transformation of the number of adaptive iterations, so as to extract the high quality internal group points. The iterative weighted least square method with iteration termination condition is applied to achieve the great processing of point groups. In this study, the effectiveness of the algorithm is verified from the three aspects of defect circle, impurity interference and other noise, and the proposed method is compared with other mainstream circle fitting algorithms. The results show that the fitting accuracy of the proposed algorithm is less than 0.7 pixels under different degrees of circular defect and impurity interference, and the algorithm performs better than other algorithms in fitting effect. In the case of the noise interference of about 20%~265%, the fitting accuracy of the algorithm does not exceed 1 pixel, and the running time is less than 0.7 s. These results indicate that the proposed algorithm can resist a large number of salt-and-pepper noise interference and maintain high accuracy and detection efficiency.

Key words: image processing, circle fitting, multilevel optimization, 3σ criterion, random sampling consensus, least square method, OpenCV, machine vision