Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network
In a structured light system, the positioning accuracy of the stripe is one of the determinants of measurement accuracy. However, the quality of the structured light stripe is reduced by noise, object shape, color, etc. The positioning accuracy of the low-quality stripe center will be decreased, and...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/2920 |
| _version_ | 1797615826245779456 |
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| author | Aozhuo Ding Qi Xue Xulong Ding Xiaohong Sun Xiaonan Yang Huiying Ye |
| author_facet | Aozhuo Ding Qi Xue Xulong Ding Xiaohong Sun Xiaonan Yang Huiying Ye |
| author_sort | Aozhuo Ding |
| collection | DOAJ |
| description | In a structured light system, the positioning accuracy of the stripe is one of the determinants of measurement accuracy. However, the quality of the structured light stripe is reduced by noise, object shape, color, etc. The positioning accuracy of the low-quality stripe center will be decreased, and the large error will be introduced into measurement results, which can only be recognized by a human. To address this problem, this paper proposes a method to identify data with relatively large errors in 3D measurement results by evaluating the quality of the grayscale distribution of stripes. In this method, the undegraded and degraded stripe images are captured. Then, the residual neural network is trained using the grayscale distribution of the two types of stripes. The captured stripes are classified by the trained model. Finally, the data corresponding to the degraded stripes, which correspond to large errors in the data, can be identified according to the classified results. The experiment shows that the algorithm proposed in this paper can effectively identify the data with large errors automatically. |
| first_indexed | 2024-03-11T07:32:24Z |
| format | Article |
| id | doaj.art-f53d24b980cf4cef92d0a4b22625cb22 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:32:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f53d24b980cf4cef92d0a4b22625cb222023-11-17T07:16:51ZengMDPI AGApplied Sciences2076-34172023-02-01135292010.3390/app13052920Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural NetworkAozhuo Ding0Qi Xue1Xulong Ding2Xiaohong Sun3Xiaonan Yang4Huiying Ye5School of Electrical and Information Engineering, Zhengzhou University, No. 100, Kexue Avenue, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, No. 100, Kexue Avenue, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, No. 100, Kexue Avenue, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, No. 100, Kexue Avenue, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, No. 100, Kexue Avenue, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, No. 100, Kexue Avenue, Zhengzhou 450001, ChinaIn a structured light system, the positioning accuracy of the stripe is one of the determinants of measurement accuracy. However, the quality of the structured light stripe is reduced by noise, object shape, color, etc. The positioning accuracy of the low-quality stripe center will be decreased, and the large error will be introduced into measurement results, which can only be recognized by a human. To address this problem, this paper proposes a method to identify data with relatively large errors in 3D measurement results by evaluating the quality of the grayscale distribution of stripes. In this method, the undegraded and degraded stripe images are captured. Then, the residual neural network is trained using the grayscale distribution of the two types of stripes. The captured stripes are classified by the trained model. Finally, the data corresponding to the degraded stripes, which correspond to large errors in the data, can be identified according to the classified results. The experiment shows that the algorithm proposed in this paper can effectively identify the data with large errors automatically.https://www.mdpi.com/2076-3417/13/5/2920structured lightstripe grayscale distributionlarge error data recognitiondeep learning |
| spellingShingle | Aozhuo Ding Qi Xue Xulong Ding Xiaohong Sun Xiaonan Yang Huiying Ye Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network Applied Sciences structured light stripe grayscale distribution large error data recognition deep learning |
| title | Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network |
| title_full | Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network |
| title_fullStr | Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network |
| title_full_unstemmed | Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network |
| title_short | Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network |
| title_sort | research on automatic error data recognition method for structured light system based on residual neural network |
| topic | structured light stripe grayscale distribution large error data recognition deep learning |
| url | https://www.mdpi.com/2076-3417/13/5/2920 |
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