Tire Defects Classification with Multi-Contrast Convolutional Neural Networks

The objective of this study is to improve the accuracy in tire defect classification with limited training samples under varying illuminations. We investigate an algorithm based on deep learning to achieve high accuracy with limited samples. First, image contrast normalizations and data augmentation were used to avoid overfitting problems of the network with a large number of parameters. Furthermore, multi-column CNN is proposed by combining several CNNs trained on differently preprocessed data into a multi-column CNN (MC-CNN), and then their predictions are averaged as the output of the proposed network. An average accuracy of 98.47% is achieved with the proposed CNN-based method. Experimental results show that our scheme receives satisfactory classification accuracy and outperforms state-of-the-art methods on the same tire defect dataset.