基于Otsu方法的钢轨图像分割

Rail image segmentation based on Otsu threshold method

由于钢轨图像灰度分布不均,一般的图像分割法难以将目标从背景中分割出来,故本文提出了目标方差加权的类间方差阈值分割法对钢轨图像进行阈值分割。分析了钢轨图像的特点,总结了加权的目标方差(Otsu)方法及其它全局阈值分割法对钢轨图像分割存在的问题。然后,对Otsu方法进行改进,以目标出现的概率为权重,对类间方差的目标方差加权,使分割阈值靠近单模直方图的左边缘和双模直方图的谷底。最后,计算图像的错误分类误差、钢轨图像的缺陷检测率和误检率来验证算法的有效性。实验结果表明,改进的Otsu方法能有效地分割钢轨图像,错误分类误差接近0。与其它阈值分割法如Otsu法、其它改进的Otsu法、最大熵阈值分割法相比,本文方法对钢轨图像的分割效果更优,缺陷检测率和误检率分别为93%和6.4%,适合机器视觉缺陷检测的实时应用。

As rail images show uneven gray distribution, general image segmenting methods can not accurately segment rail images. To address this issue, this paper presents an improved Otsu method using weighted object variance（WOV） for rail image segmentation to separate the defect from its background. Firstly, the property of a rail image was analyzed and the problems of the Otsu method and other global threshold methods for segmenting rail images were summarized. Then, the Otsu method was improved. By taking the cumulative probability of defect occurrence for the weighting, the object variance of between-class variance was weighted, and the threshold will always be a value that locates at two peaks or at the left bottom rim of a single peak histogram. Finally, the misclassification error （MCE）, the detection rate and false alarm rate of the defect image were calculated to validate the effectiveness of proposed method. The experimental results demonstrate thatthe improved Otsu method accurately segments various kinds of rail images and the MCE value is close to 0. As comparing to the Otsu method, other improved Otsu method and maximum entropy threshold method, the proposed method provides better segmentation results, the detection rate and false alarm rate for the rail defected image are 93% and 6. 4% respectively. It is suitable for the applications in machine vision defect detection in real time.