基于支持向量回归的零件直线边缘亚像素图像检测

Mechanical part linear edge sub-pixel image detection based on support vector regression

以圆锥螺纹为研究对象,讨论了基于支持向量回归的机械零件直线边缘的亚像素图像检测方法。采用中值滤波、二值变换等算法,对通过电荷耦合器件采集的圆锥螺纹图像进行处理,获得了螺纹牙形的直线部分的像素表征,并以此构成训练集,对回归型支持向量机进行训练,得到了螺纹牙形的直线方程表示。通过支持向量回归获得的拟合直线是螺纹牙形的亚像素表示,据此对锥螺纹的主要参数进行检测,大大降低了电荷耦合器件的离散性和系统噪声对测量结果的影响。实验表明,本方法具有测量速度较快、测量精度较高的特点。

Taking the conical thread as the object of study, a sub-pixel image detection method for the mechanical part linear edge based on the support vector regression was proposed. Through processing the conical thread image recorded by the charge coupled device（CCD） , the pixel characterization of the straight portion of the thread form could be obtained with the algorithm of median filter, binary transform etc, to make up the training set. The support vector machine for regression was trained by the training set and the straight line equation of the thread form was obtained which is the expression of the sub-pixel . Making use of it to detect the main parameters of the conical thread, the effect of the discreteness of the CCD and the system noise on the detection results were significantly reduced. The experiment results show that the suggested method is characterized by high detection speed and high precision.