基于DT-CWT和SVM的踏面旋转不变纹理提取算法

针对列车车轮踏面旋转纹理信息无法准确、有效提取的问题，提出一种基于Radon变换和双树复小波变换（DT-CWT）的列车车轮踏面特征提取方法。首先，对车轮踏面图像进行Radon变换；然后，对变换后的图像进行DT-CWT分解，使用分解后的各层低频子带系数和高频子带系数模的均值和标准方差构造特征向量，将其作为区分列车车轮踏面是否发生损伤的依据；最后，由支持向量机（SVM）进行分类决策。使用动车所采集的图像及人为加噪声后的图像进行分类实验，结果表明，本文使用的Radon和DT-CWT算法能有效地进行旋转不变纹理的提取，SVM分类正确率可以达到95%，可为列车车轮踏面状况检测提供更为准确便捷的方法支撑。

Rotation Invariant Texture Extraction of Wheel Tread Based on DT-CWT and SVM

Aiming at the problem that the rotation texture information of train wheel treads cannot be extracted accurately and effectively, a method for extracting train wheel tread features based on Radon transform and dual-tree complex wavelet transform (DT-CWT) is proposed. Firstly, the Radon transform is performed on the image of the wheel tread; then, the transformed image is decomposed by DT-CWT, and the decomposed layer of the low-frequency sub-band coefficients and the modulus of the mean and standard deviation of the high-frequency sub-band coefficients are used to construct the feature vector, and the feature is used as the basis for distinguishing whether the train wheel tread is damaged or not; finally, the classification decision is made by the support vector machine (SVM). Part of the images used in the classification test are from the automatic vehicle station, and part of the images are artificially noised. The results show that the Radon and DT-CWT algorithms used in this paper can effectively perform the rotation invariant texture extraction, and the SVM classification accuracy rate can reach 95%. It provides more accurate and convenient method support for the detection of train wheel tread conditions.