| DC Field | Value | Language |
|---|
| dc.contributor.author | Ganchenko, V. | - |
| dc.contributor.author | Starovoitov, V. | - |
| dc.contributor.author | Xiangtao Zheng | - |
| dc.date.accessioned | 2021-11-19T06:10:23Z | - |
| dc.date.available | 2021-11-19T06:10:23Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.citation | Ganchenko, V. Image Semantic Segmentation Based on High-Resolution Networks for Monitoring Agricultural Vegetation / Ganchenko V., Starovoitov V., Xiangtao Zheng // 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), September 1–4, 2020, Timisoara, Romania. – Romania, 2020. – P. 264–269. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/45955 | - |
| dc.description.abstract | In the article, recognition of state of agricultural vegetation from aerial photographs at various spatial resolutions was considered. Proposed approach is based on a semantic segmentation using convolutional neural networks. Two variants of High-Resolution network architecture (HRNet) are described and used. These neural networks were trained and applied to aerial images of agricultural fields. In our experiments, accuracy of four land classes recognition (soil, healthy vegetation, diseased vegetation and other objects) was about 93-94%. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | IEEE | ru_RU |
| dc.subject | публикации ученых | ru_RU |
| dc.subject | convolutional neural network | ru_RU |
| dc.subject | semantic segmentation | ru_RU |
| dc.subject | aerial photograph | ru_RU |
| dc.subject | agricultural vegetation | ru_RU |
| dc.title | Image Semantic Segmentation Based on High-Resolution Networks for Monitoring Agricultural Vegetation | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Публикации в зарубежных изданиях
|
