Tyre Pattern Classification Based on Multi-scale GCN Model

Abstract

Tyre pattern image classification plays an important role in traffic accidents and criminal scene investigation, and it contains rich texture structure information. Classic deep learning models, such as VGG, are often not targeted to represent the texture structure of tyre pattern images, and often cause over-fitting training due to large-scale parameters and insufficient training samples. To improve classification performance of tyre pattern image and solve the model overfitting problem, an efficient tyre pattern image classification model based on multi-scale Gabor convolutional neural network (MS-GCN) is proposed. First, a bank of large-scale directional Gabor filters are used to modulate the convolution kernel to extract more accurate texture features for large-size tyre pattern images, which greatly reduces the number of the training parameters and makes the model more streamlined. Secondly, due to the multi-scale texture similarity of the tyre pattern image, the multi-scale features from different convolutional layers are fused to produce the precise feature representation of the image, following by the optimal feature dimension selection. A large number of experiments were carried out on the real tyre pattern image data set. The results showed that the classification accuracy of the proposed algorithm is 95.9%, which is greatly improved compared with the handcrafted feature extraction algorithm and increased by 17.3% compared with deep learning-based model VGG16. In addition, the classification accuracy of the proposed algorithm on the GHIM-10K data set is 92%, which is also significantly improved compared to other methods. Overall, it shows the effectiveness and superiority of the proposed algorithm.