A Circle Detection Method Based on Edge Detection and Fitting Curve Clustering

Abstract

The invention discloses a circle detection method based on edge detection and fitted curve clustering. The method includes the following steps that first, the edge of a grey-scale map is detected, edge curves with the pixel number smaller than a pixel threshold are removed, and then a curve set T is obtained; second, the gradient direction vector of each edge point in the curve set T in the grey-scale map is calculated and recorded; third, candidate curve marking and parameter fitting based on edge gradient direction constraint are performed, wherein in the third step, firstly, estimated circle centers and estimated radii are calculated, secondly, the direction vectors of the edge points of each curve to the corresponding estimated circle center are calculated, thirdly, candidate points are marked based on the gradient direction constraint, fourthly, a candidate curve set is determined, and fifthly, circle parameters of the candidate curves are calibrated; fourth, the candidate curves are clustered and fused, and then the fused circle center and the fused radius are obtained; fifth, the detection result is output according to the completeness degree of the circle. The method has the advantages of being good in adaptability, high in calculation speed, good in noise immunity, little in resource consumption and capable of being widely applied in the field of circular curve detection.

Description

technical field [0001] The invention relates to circular curve detection for objects with circular structures in industrial production, in particular to a circle detection method based on edge detection and fitting curve clustering. Background technique [0002] In industrial production, machine vision technology has been widely used in the field of target detection and recognition. Compared with traditional detection methods, this technology not only has high speed, high precision, and good reliability, but also greatly improves production efficiency. In view of this, this technology has been adopted by more and more manufacturers. Since man-made objects often have circular structural features, circle detection has important research significance in industrial applications, such as machine vision-based dispensing machines, when identifying the dispensing position of devices with circular structures such as camera modules , it is necessary to detect circular structures quic...

AI Technical Summary

Problems solved by technology

However, due to the need to carry out cumulative calculations in the three-dimensional parameter space, the amount of calculation is large and the resources are too much. Moreover, when processing images with complex background interference, a large amount of useless accumulation will be introduced, which will greatly reduce the performance of the algorithm, and even make it impossible to effectively extract circle

The curve fitting method is sensitive to outliers, so it will produce a large detection error in the case of complex background and blurred circle edges.

In addition, in addition to background interference factors, there are also interference factors such as changes in lighting conditions and unevenness in actual industrial applications, which often lead to blurred or discontinuous edges of circles, affecting the accuracy and efficiency of circle detection methods

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