Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding
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 embed...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2018-01-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25895960/view |
| _version_ | 1818006401864171520 |
|---|---|
| author | Yan Zhang Xuehong Cui Yun Liu Bin Yu |
| author_facet | Yan Zhang Xuehong Cui Yun Liu Bin Yu |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:00:40Z |
| format | Article |
| id | doaj.art-64efafa0b213497f8930228813f29615 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-14T05:00:40Z |
| publishDate | 2018-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-64efafa0b213497f8930228813f296152022-12-22T02:10:58ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832018-01-0111110.2991/ijcis.11.1.80Tire Defects Classification Using Convolution Architecture for Fast Feature EmbeddingYan ZhangXuehong CuiYun LiuBin YuConvolutional 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.https://www.atlantis-press.com/article/25895960/viewDeep learningDefect classificationCNNAlexNetTire defects |
| spellingShingle | Yan Zhang Xuehong Cui Yun Liu Bin Yu Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding International Journal of Computational Intelligence Systems Deep learning Defect classification CNN AlexNet Tire defects |
| title | Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding |
| title_full | Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding |
| title_fullStr | Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding |
| title_full_unstemmed | Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding |
| title_short | Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding |
| title_sort | tire defects classification using convolution architecture for fast feature embedding |
| topic | Deep learning Defect classification CNN AlexNet Tire defects |
| url | https://www.atlantis-press.com/article/25895960/view |
| work_keys_str_mv | AT yanzhang tiredefectsclassificationusingconvolutionarchitectureforfastfeatureembedding AT xuehongcui tiredefectsclassificationusingconvolutionarchitectureforfastfeatureembedding AT yunliu tiredefectsclassificationusingconvolutionarchitectureforfastfeatureembedding AT binyu tiredefectsclassificationusingconvolutionarchitectureforfastfeatureembedding |