针对空轨轨道梁的表面缺陷，采用图像分析的方法进行检测。对于电荷耦合器件（CCD）摄像机采集到的钢板表面图像信息，先通过基于均值和方差的粗检方法，获取正常样本，即利用一定大小的滑动窗口计算表面图像的均值和方差，分析确定阈值后将样本分成疑似缺陷样本和正常样本。接着在粗检的基础上，采用基于积分图的Bayes细检方法提高准确度，即用压缩感知算法得到缺陷的特征样本，进一步将粗检过程中错分到缺陷样本中的正常样本剔除。试验结果表明，该方法对钢板表面缺陷的检出率达到98%以上，准确率达到95%以上。

Aiming at the surface defect problem of sky railway track beam, the method of image analysis is adopted to inspect. Firstly, for the image information of steel plate surface which is collected by CCD camera, the normal samples are obtained by means of a rough inspection method based on mean and variance. The mean and variance of surface images are calculated by sliding windows of a certain size, and the samples are divided into suspected defect samples and normal samples after the threshold is determined. Then, on the basis of rough inspection, Bayes fine inspection method based on integral graph is adopted to improve the accuracy. Using the compressed sensing algorithm, the characteristic samples of defects are obtained, and the normal samples which are wrongly divided into defect samples in the rough inspection process can be removed. The experimental results show that the detection rate and accuracy of this method for surface defects of steel plate are greater than 98% and 95% respectively.