| DC Field | Value | Language |
|---|
| dc.contributor.author | Shijie Chen | - |
| dc.contributor.author | Xinpeng Lu | - |
| dc.contributor.author | Jiashuo Sun | - |
| dc.contributor.author | Zhichao Yin | - |
| dc.contributor.author | Moufa Hu | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2025-09-10T06:43:50Z | - |
| dc.date.available | 2025-09-10T06:43:50Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.citation | Real-time detection of multi-scale miniature unmanned aerial vehicles based on YOLOv9 / Shijie Chen, Xinpeng Lu, Jiashuo Sun [et al.] // Информационная безопасность : сборник материалов 61-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 21–25 апреля 2025 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2025. – С. 133–136. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/61512 | - |
| dc.description.abstract | Aiming at the security risks of unregulated and unmanned aerial vehicles (UAVs), this paper proposes a new real-time detection method based on YOLOv9, which integrates reversible functions, programmable gradient information, and a generalized high-efficiency layer aggregation network, and combined with downsampling and local feature training method. Experiments show that the detection accuracy of the method is more than 90% and the processing frame rate is more than 20Hz. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | unmanned aerial vehicles | en_US |
| dc.subject | detection methods | en_US |
| dc.subject | YOLOv9 | en_US |
| dc.title | Real-time detection of multi-scale miniature unmanned aerial vehicles based on YOLOv9 | en_US |
| Appears in Collections: | Информационная безопасность : материалы 61-й научной конференции аспирантов, магистрантов и студентов (2025)
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