Tire Defects Classification with Multi-Contrast Convolutional Neural Networks

Author:

Cui Xuehong1,Liu Yun2,Zhang Yan3,Wang Chuanxu1

Affiliation:

1. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, P. R. China

2. Development Planning Office, Qingdao University of Science and Technology, Qingdao 266061, P. R. China

3. School of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, P. R. China

Abstract

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.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Applied Basic Research Project of Qingdao

Doctoral Found of QUST

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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1. End-to-end tire defect detection model based on transfer learning techniques;Neural Computing and Applications;2024-04-22

2. Research on an Improved Car Tire Defect Detection Algorithm based on YOLOv5s;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19

3. Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study;Engineering Research Express;2024-02-14

4. Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture;Measurement Science and Technology;2023-05-23

5. Defect detection in composites by deep learning using solitary waves;International Journal of Mechanical Sciences;2023-02

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