Recognition of Tire Tread Patterns Based on Gabor Wavelets and Support Vector Machine

In this paper, we propose a novel algorithm based on Gabor wavelets and support vector machine (SVM) for recognition of tire tread patterns. Input tire images are first preprocessed by morphological opening to enhance the features (or textures) on tire surface. The grooves in tire surface are salient important features for a tire matching system. We detect the tire tread patterns of being grooved or wavy and use this feature to train various SVM classifiers. The features of tire tread patterns are then represented by Gabor wavelets, and feature extraction is further carried out by principal component analysis (PCA). Finally, the matching processes are achieved by the classifiers of SVM, Euclidean distance and cosine distance. Result shows that the recognition rate of 60% for tire images can be obtained by the SVM classifier when 15 tire tread patterns are used.