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YOLO11 and ByteTrack improve bee monitoring accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a system for monitoring bee activity at hive entrances using YOLO11 for detection and ByteTrack for tracking. The study found that progressive backbone unfreezing and moderate data augmentation yielded the best detection results, achieving 97.0% precision and 98.7% mAP50. Optimization of ByteTrack parameters improved trajectory continuity, leading to a system that correctly counted 91.5% of incoming bees in a test video, though outgoing bee counts were less accurate due to missed detections from rapid motion and blur. AI

IMPACT Demonstrates a practical application of object detection and tracking models for ecological monitoring.

RANK_REASON Academic paper detailing a specific application of computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YOLO11 and ByteTrack improve bee monitoring accuracy

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Academic paper detailing a specific application of computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Thi Thu Thao Nguyen, Johannes Reschke ·

    Bee Detection and Tracking at Hive Entrance using YOLO11 and ByteTrack

    arXiv:2608.23213v1 Announce Type: new 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 paramet…

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