Problem
Research questions and friction points this paper is trying to address.
bee detection
tracking
hive entrance
Bee Detection and Tracking at Hive Entrance using YOLO11 and ByteTrack
🤖 AI Summary
研究使用YOLO11和ByteTrack算法解决蜂巢入口蜜蜂检测与跟踪问题，通过适度数据增强、逐步解冻骨干网络及优化跟踪参数提高系统准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
YOLO11
ByteTrack
progressive backbone unfreezing
data augmentation
parameter optimization
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