Deep hashing network for material defect image classification
Common non‐destructive material testing technology has some well‐known problems such as slow detection, low detection accuracy, and low level of information obtained. To solve these problems, this study applied recent advances in convolution neural networks to propose an effective deep learning netw...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-12-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5286 |
| _version_ | 1797684202977624064 |
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| author | Kai Yang Zhiyi Sun Anhong Wang Ruizhen Liu Qianlai Sun Yin Wang |
| author_facet | Kai Yang Zhiyi Sun Anhong Wang Ruizhen Liu Qianlai Sun Yin Wang |
| author_sort | Kai Yang |
| collection | DOAJ |
| description | Common non‐destructive material testing technology has some well‐known problems such as slow detection, low detection accuracy, and low level of information obtained. To solve these problems, this study applied recent advances in convolution neural networks to propose an effective deep learning network using casting datasets. The approach achieves non‐destructive material testing with automatic, intelligent detection technology. For most existing deep learning networks, an image is eventually transformed into a multidimensional visual feature vector for comparison and classification. However, such vectors may not optimally improve detection precision and speed, and can lead to significant storage problems. A deep hashing network is proposed in which images are mapped into compact binary codes. There are three key components: (i) a sub‐network with multiple convolution‐pooling layers to capture image representations; (ii) a hashing layer to generate compact binary hash codes; (iii) an encoder module to divide the image feature vector from the output of the sub‐network above into multiple branches, each encoded into one hash bit. Extensive experiments using a casting dataset show promising performance compared with the state‐of‐the‐art approach. |
| first_indexed | 2024-03-12T00:26:14Z |
| format | Article |
| id | doaj.art-67f50d1554e246e7b6a9a38a160c626a |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:26:14Z |
| publishDate | 2018-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-67f50d1554e246e7b6a9a38a160c626a2023-09-15T10:32:11ZengWileyIET Computer Vision1751-96321751-96402018-12-011281112112010.1049/iet-cvi.2018.5286Deep hashing network for material defect image classificationKai Yang0Zhiyi Sun1Anhong Wang2Ruizhen Liu3Qianlai Sun4Yin Wang5Taiyuan University of Science and TechnologyTaiyuanShanxiPeople's Republic of ChinaTaiyuan University of Science and TechnologyTaiyuanShanxiPeople's Republic of ChinaTaiyuan University of Science and TechnologyTaiyuanShanxiPeople's Republic of ChinaTaiyuan University of Science and TechnologyTaiyuanShanxiPeople's Republic of ChinaTaiyuan University of Science and TechnologyTaiyuanShanxiPeople's Republic of ChinaTaiyuan University of Science and TechnologyTaiyuanShanxiPeople's Republic of ChinaCommon non‐destructive material testing technology has some well‐known problems such as slow detection, low detection accuracy, and low level of information obtained. To solve these problems, this study applied recent advances in convolution neural networks to propose an effective deep learning network using casting datasets. The approach achieves non‐destructive material testing with automatic, intelligent detection technology. For most existing deep learning networks, an image is eventually transformed into a multidimensional visual feature vector for comparison and classification. However, such vectors may not optimally improve detection precision and speed, and can lead to significant storage problems. A deep hashing network is proposed in which images are mapped into compact binary codes. There are three key components: (i) a sub‐network with multiple convolution‐pooling layers to capture image representations; (ii) a hashing layer to generate compact binary hash codes; (iii) an encoder module to divide the image feature vector from the output of the sub‐network above into multiple branches, each encoded into one hash bit. Extensive experiments using a casting dataset show promising performance compared with the state‐of‐the‐art approach.https://doi.org/10.1049/iet-cvi.2018.5286intelligent detection technologymultidimensional visual feature vectordeep hashing networkmultiple convolution-pooling layersimage representationscompact binary hash codes |
| spellingShingle | Kai Yang Zhiyi Sun Anhong Wang Ruizhen Liu Qianlai Sun Yin Wang Deep hashing network for material defect image classification IET Computer Vision intelligent detection technology multidimensional visual feature vector deep hashing network multiple convolution-pooling layers image representations compact binary hash codes |
| title | Deep hashing network for material defect image classification |
| title_full | Deep hashing network for material defect image classification |
| title_fullStr | Deep hashing network for material defect image classification |
| title_full_unstemmed | Deep hashing network for material defect image classification |
| title_short | Deep hashing network for material defect image classification |
| title_sort | deep hashing network for material defect image classification |
| topic | intelligent detection technology multidimensional visual feature vector deep hashing network multiple convolution-pooling layers image representations compact binary hash codes |
| url | https://doi.org/10.1049/iet-cvi.2018.5286 |
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