Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
In order to avoid the dependence of traditional sub-pixel displacement methods on interpolation method calculation, image gradient calculation, initial value estimation and iterative calculation, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, and a square da...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2868 |
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| author | Yongxing Lin Xiaoyan Xu Zhixin Tie |
| author_facet | Yongxing Lin Xiaoyan Xu Zhixin Tie |
| author_sort | Yongxing Lin |
| collection | DOAJ |
| description | In order to avoid the dependence of traditional sub-pixel displacement methods on interpolation method calculation, image gradient calculation, initial value estimation and iterative calculation, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, and a square dataset expansion method is also proposed to rapidly expand the training dataset. The ST-SDM computes sub-pixel displacement values of different scales through three-level classification tasks, and solves the problem of positive and negative displacement with the rotation relative tag value method. The accuracy of the ST-SDM is verified by simulation experiments, and its robustness is verified by real rigid body experiments. The experimental results show that the ST-SDM model has higher accuracy and higher efficiency than the comparison algorithm. |
| format | Article |
| id | doaj-art-a0ab1a5ae694400a8cd38f29b2eef0ad |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a0ab1a5ae694400a8cd38f29b2eef0ad2025-08-20T02:59:13ZengMDPI AGApplied Sciences2076-34172025-03-01155286810.3390/app15052868Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification ApproachYongxing Lin0Xiaoyan Xu1Zhixin Tie2Keyi College, Zhejiang Sci-Tech University, Shaoxing 312369, ChinaSUPCON Technology Co., Ltd., Hangzhou 310053, ChinaKeyi College, Zhejiang Sci-Tech University, Shaoxing 312369, ChinaIn order to avoid the dependence of traditional sub-pixel displacement methods on interpolation method calculation, image gradient calculation, initial value estimation and iterative calculation, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, and a square dataset expansion method is also proposed to rapidly expand the training dataset. The ST-SDM computes sub-pixel displacement values of different scales through three-level classification tasks, and solves the problem of positive and negative displacement with the rotation relative tag value method. The accuracy of the ST-SDM is verified by simulation experiments, and its robustness is verified by real rigid body experiments. The experimental results show that the ST-SDM model has higher accuracy and higher efficiency than the comparison algorithm.https://www.mdpi.com/2076-3417/15/5/2868digital speckle correlation methoddigital image correlation methodsub-pixel displacement methoddeep learningsquare dataset expansion method |
| spellingShingle | Yongxing Lin Xiaoyan Xu Zhixin Tie Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach Applied Sciences digital speckle correlation method digital image correlation method sub-pixel displacement method deep learning square dataset expansion method |
| title | Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach |
| title_full | Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach |
| title_fullStr | Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach |
| title_full_unstemmed | Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach |
| title_short | Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach |
| title_sort | sub pixel displacement measurement with swin transformer a three level classification approach |
| topic | digital speckle correlation method digital image correlation method sub-pixel displacement method deep learning square dataset expansion method |
| url | https://www.mdpi.com/2076-3417/15/5/2868 |
| work_keys_str_mv | AT yongxinglin subpixeldisplacementmeasurementwithswintransformerathreelevelclassificationapproach AT xiaoyanxu subpixeldisplacementmeasurementwithswintransformerathreelevelclassificationapproach AT zhixintie subpixeldisplacementmeasurementwithswintransformerathreelevelclassificationapproach |