Machine learning-based optimization of photogrammetric JRC accuracy
Abstract To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-77054-w |
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| author | Qinzheng Yang Ang Li Yipeng Liu Hongtian Wang Zhendong Leng Fei Deng |
| author_facet | Qinzheng Yang Ang Li Yipeng Liu Hongtian Wang Zhendong Leng Fei Deng |
| author_sort | Qinzheng Yang |
| collection | DOAJ |
| description | Abstract To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed. Combining SPSA, portable positioning plate, and moving camera capture strategy, a photogrammetric experiment for small-scale rock samples in the field was conducted, collecting 48 datasets with different shooting parameters. Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. Finally, the applicability and limitations of the newly proposed method were further discussed. |
| format | Article |
| id | doaj-art-0b2d5432ab3747969b64bfcf654f2383 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0b2d5432ab3747969b64bfcf654f23832025-08-20T02:50:06ZengNature PortfolioScientific Reports2045-23222024-11-0114111510.1038/s41598-024-77054-wMachine learning-based optimization of photogrammetric JRC accuracyQinzheng Yang0Ang Li1Yipeng Liu2Hongtian Wang3Zhendong Leng4Fei Deng5School of Highway, Chang’an UniversitySchool of Highway, Chang’an UniversitySchool of Energy and Electrical Engineering, Chang’an UniversitySchool of Highway, Chang’an UniversityChina Gezhouba Group Explosive Co., LtdNorth China Institute of Science and TechnologyAbstract To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed. Combining SPSA, portable positioning plate, and moving camera capture strategy, a photogrammetric experiment for small-scale rock samples in the field was conducted, collecting 48 datasets with different shooting parameters. Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. Finally, the applicability and limitations of the newly proposed method were further discussed.https://doi.org/10.1038/s41598-024-77054-wPhotogrammetryJoint roughness coefficientJRC optimization3D reconstruction |
| spellingShingle | Qinzheng Yang Ang Li Yipeng Liu Hongtian Wang Zhendong Leng Fei Deng Machine learning-based optimization of photogrammetric JRC accuracy Scientific Reports Photogrammetry Joint roughness coefficient JRC optimization 3D reconstruction |
| title | Machine learning-based optimization of photogrammetric JRC accuracy |
| title_full | Machine learning-based optimization of photogrammetric JRC accuracy |
| title_fullStr | Machine learning-based optimization of photogrammetric JRC accuracy |
| title_full_unstemmed | Machine learning-based optimization of photogrammetric JRC accuracy |
| title_short | Machine learning-based optimization of photogrammetric JRC accuracy |
| title_sort | machine learning based optimization of photogrammetric jrc accuracy |
| topic | Photogrammetry Joint roughness coefficient JRC optimization 3D reconstruction |
| url | https://doi.org/10.1038/s41598-024-77054-w |
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