Abstract: 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.

Key words: Radon transform； dual tree complex wavelet transform； rotation invariant； feature extraction； support vector machine