Bee Detection and Tracking at Hive Entrance using YOLO11 and ByteTrack
This work presents an automatic bee entrance monitoring system based on YOLO11 transfer learning and the ByteTrack tracking algorithm. The study investigates the influence of data augmentation, backbone freezing, and tracker parameter optimization on the detection and counting of small, fast-moving bees. The detector with progressive backbone unfreezing strategy achieved about 97.0% precision and 98.7% mAP50, while providing more stable convergence than full fine-tuning. Experiments also showed that light augmentation outperformed heavy augmentation. For tracking, ByteTrack parameters were optimized to improve trajectory continuity under low-confidence detections. On an independent 25 FPS...
| Authors | Thi Thu Thao Nguyen, Johannes Reschke |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 4 October 2026 |
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