用YOLO11和ByteTrack实现蜂群入口自动监测，提升小蜜蜂检测计数准确率。
Bee Detection and Tracking at Hive Entrance using YOLO11 and ByteTrack
- 采用渐进式解冻主干网络，提升模型收敛稳定性。
- 优化后系统在侧视视频中识别出43只入蜂（91.5%），7只出蜂（23.3%）。
- 轻度数据增强优于重度增强，参数调优减少追踪中断。
本研究基于YOLO11迁移学习与ByteTrack追踪算法，构建自动蜂群入口监控系统。探究了数据增强、主干网络冻结及追踪器参数优化对小型快速移动蜜蜂检测与计数的影响。采用渐进式解冻策略的检测器达到约97.0%精确率和98.7% mAP50，收敛更稳定；实验表明轻度增强优于重度增强。追踪方面，通过优化ByteTrack参数，提升了低置信度检测下的轨迹连续性。在独立25 FPS侧视视频上，系统正确识别43/47只入蜂（91.5%）和7/30只出蜂（23.3%）。错误分析显示主要误差源于高速运动与运动模糊导致的漏检，而追踪失败经调优后显著减少。结果表明，适度增强、渐进解冻与参数优化可提升真实场景下蜂群监控的可靠性。
原文摘要 · Abstract (English)
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 side-view video, the optimized YOLO11-ByteTrack system correctly counted 43 of 47 incoming bees (91.5%) and 7 of 30 outgoing bees (23.3%). Error analysis showed that most counting errors were caused by missed detections due to rapid bee motion and motion blur, while tracking failures became less frequent after parameter optimization. Overall, the results indicate that moderate augmentation, progressive backbone unfreezing, and ByteTrack tuning improve the reliability of automatic bee entrance monitoring under realistic recording conditions.
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