发表机构
Savonia University of Applied Sciences; Ostbayerische Technische Hochschule Regensburg（萨沃尼亚应用科学大学; 雷根斯堡东巴伐利亚应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于YOLO11与ByteTrack的自动化蜂巢入口蜜蜂监测系统，通过优化数据增强、骨干网络解冻策略及跟踪器参数，提升了蜜蜂检测与计数的可靠性，在25 FPS侧视视频中入巢蜜蜂计数准确率达91.5%。
AI 中文摘要
本研究提出一种基于YOLO11迁移学习和ByteTrack跟踪算法的自动化蜂巢入口监测系统，探究了数据增强、骨干网络冻结及跟踪器参数优化对小型快速移动蜜蜂检测与计数的影响。采用渐进式骨干网络解冻策略的检测器，精度约达97.0%，mAP50达98.7%，且收敛性比全微调更稳定；实验还表明轻量型数据增强优于重度增强。针对跟踪环节，优化了ByteTrack参数以提升低置信度检测下的轨迹连续性。在独立的25 FPS侧视视频中，优化后的YOLO11-ByteTrack系统正确计数47只入巢蜜蜂中的43只（准确率91.5%），30只出巢蜜蜂中的7只（准确率23.3%）。误差分析显示，多数计数误差源于蜜蜂快速运动导致的漏检和运动模糊，参数优化后跟踪失败频率降低。总体而言，适度数据增强、渐进式骨干网络解冻及ByteTrack调优可提升实际记录条件下自动化蜂巢入口监测的可靠性。
英文摘要
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.
Comments17 pages, 13 figures
