The segmentation of nanoparticles with a novel approach of HRU2-Net†
Abstract Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86085-w |
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| author | Yu Zhang Heng Zhang Fengfeng Liang Guangjie Liu Jinlong Zhu |
| author_facet | Yu Zhang Heng Zhang Fengfeng Liang Guangjie Liu Jinlong Zhu |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Abstract Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this study, we proposed HRU2-Net†, an enhancement of the U2-Net† model, featuring multi-level semantic information fusion. This approach exhibits strong competitiveness and refined segmentation capabilities for nanoparticle segmentation. It achieves a Mean intersection over union (MIoU) of 87.31%, with an accuracy rate exceeding 97.31%, leading to a significant improvement in segmentation effectiveness and precision. The results show that the deep learning-based method significantly enhances the efficacy of nanomaterial research, which holds substantial significance for the advancement of nanomaterial science. |
| format | Article |
| id | doaj-art-1fe939e7c50b4cadbbef7f48fd0cc9e6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1fe939e7c50b4cadbbef7f48fd0cc9e62025-01-19T12:17:38ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-025-86085-wThe segmentation of nanoparticles with a novel approach of HRU2-Net†Yu Zhang0Heng Zhang1Fengfeng Liang2Guangjie Liu3Jinlong Zhu4School of Computer Science and Technology, Changchun Normal UniversitySchool of Computer Science and Technology, Changchun Normal UniversitySchool of Computer Science and Technology, Changchun Normal UniversitySchool of Computer Science and Technology, Changchun Normal UniversitySchool of Computer Science and Technology, Changchun Normal UniversityAbstract Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this study, we proposed HRU2-Net†, an enhancement of the U2-Net† model, featuring multi-level semantic information fusion. This approach exhibits strong competitiveness and refined segmentation capabilities for nanoparticle segmentation. It achieves a Mean intersection over union (MIoU) of 87.31%, with an accuracy rate exceeding 97.31%, leading to a significant improvement in segmentation effectiveness and precision. The results show that the deep learning-based method significantly enhances the efficacy of nanomaterial research, which holds substantial significance for the advancement of nanomaterial science.https://doi.org/10.1038/s41598-025-86085-wNanoparticlesDeep learningHRU2-Net† modelImage segmentation |
| spellingShingle | Yu Zhang Heng Zhang Fengfeng Liang Guangjie Liu Jinlong Zhu The segmentation of nanoparticles with a novel approach of HRU2-Net† Scientific Reports Nanoparticles Deep learning HRU2-Net† model Image segmentation |
| title | The segmentation of nanoparticles with a novel approach of HRU2-Net† |
| title_full | The segmentation of nanoparticles with a novel approach of HRU2-Net† |
| title_fullStr | The segmentation of nanoparticles with a novel approach of HRU2-Net† |
| title_full_unstemmed | The segmentation of nanoparticles with a novel approach of HRU2-Net† |
| title_short | The segmentation of nanoparticles with a novel approach of HRU2-Net† |
| title_sort | segmentation of nanoparticles with a novel approach of hru2 net† |
| topic | Nanoparticles Deep learning HRU2-Net† model Image segmentation |
| url | https://doi.org/10.1038/s41598-025-86085-w |
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