Research On The Method Of Circular Target Recognition

Automatic detection of circular objects is the actual demand in real life and industrial manufacturing,which is also one of the most basic and important fields in computer vision.The most common used algorithms of circular detection include Hough transform,roundness detection,template matching,and proposed in recent years by linear segmentation.For the problems of low detection efficiency and low accuracy in the complex background,a joint algorithm that combines the model of support vector regression with the three-point fitting circle detection algorithm is proposed in this paper.The main research works of this article are as follows:(1)Analyze and compare commonly used image preprocessing methods,including image contrast enhancement,edge detection operators,and image thresholding methods.Choosing an efficient and reasonable pre-processing method is the first and important step to improve the visual effect of the image and the computer recognition efficiency.(2)From the current situation of scholars at home and abroad in the circular detection,an algorithm combining the support vector regression with a three-point fitting circle is proposed in this paper.This algorithm trains different types of circular samples by supporting a vector regression model to obtain an regression function.Taking this function as the centerline,one similar circular ring with a certain width can be constructed.The points in this interval are considered as the circular boundary points.Then the center and radius can be calculated based on the three-point fitting circular geometry algorithm,so as to achieve the purpose of identifying the circle.(3)Through VS2010 + OpenCV2.4.9 platform,the proposed algorithm is compared with classic circular detection methods in two aspects: accuracy and time complexity.The experimental results show that the circular boundary information can be obtained from the relatively noisy background images by learning the training samples thereby determining the location of the circle,which has some advantages over using only a certain circular recognition algorithm.In the field of machine vision positioning with circles,this joint algorithm has important theoretical research value and practical significance.