Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding

Abstract

Convolutional Neural Network (CNN) has become an increasingly important research field in machine learning and computer vision. Deep image features can be learned and subsequently used for detection, classification and retrieval tasks in an end-to-end model. In this paper, a supervised feature embedded deep learning based tire defects classification method is proposed. We probe into deep learning based image classification problems with application to real-world industrial tasks. Combined regularization techniques are applied for training to boost the performance. Experimental results show that our scheme receives satisfactory classification accuracy and outperforms state-of-the-art methods.