| DC Field | Value | Language |
|---|
| dc.contributor.author | Guo Qicheng | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2025-05-02T08:06:17Z | - |
| dc.date.available | 2025-05-02T08:06:17Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.citation | Guo Qicheng. Vehicle and label detection method based on YOLOv11 / Guo Qicheng // Big Data и анализ высокого уровня = Big Data and Advanced Analytics : сборник научных статей XI Международной научно-практической конференции, Республика Беларусь, Минск, 23–24 апреля 2025 года / Белорусский государственный университет информатики и радиоэлектроники [и др.] ; редкол.: В. А. Богуш [и др.]. – Минск, 2025. – С. 340–342. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/59677 | - |
| dc.description.abstract | This paper proposes a vehicle and label detection method based on the YOLOv11 algorithm. By constructing a custom dataset of vehicles and labels, the YOLOv11 model is trained to achieve precise detection of vehicles and their rear-mounted labels. This paper details the dataset creation process, label design, model training procedure, and the selection of optimal training parameters. Finally, the algorithm's performance is evaluated using a validation set. Experimental results demonstrate that the YOLOv11-based vehicle and label detection method exhibits strong performance in terms of accuracy and real-time capability, meeting the requirements of practical applications. | en_US |
| dc.language.iso | ru | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | оbject detection | en_US |
| dc.subject | vehicles | en_US |
| dc.subject | computer vision | en_US |
| dc.title | Vehicle and label detection method based on YOLOv11 | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | BIG DATA and Advanced Analytics = BIG DATA и анализ высокого уровня : сборник научных статей (2025)
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