Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding

DOI

10.2991/ijcis.11.1.80How to use a DOI?

Keywords

Deep learning; Defect classification; CNN; AlexNet; Tire defects

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Yan Zhang
AU  - Xuehong Cui
AU  - Yun Liu
AU  - Bin Yu
PY  - 2018
DA  - 2018/05/23
TI  - Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding
JO  - International Journal of Computational Intelligence Systems
SP  - 1056
EP  - 1066
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.80
DO  - 10.2991/ijcis.11.1.80
ID  - Zhang2018
ER  -