OpenBeePose: A Remote Sensing Framework for Bee Pose Estimation Using Deep Learning

Sajad Sabzi

doi:10.3390/app16010303

2025-12-28

Sajad Sabzi *

Ali Najar

Raziyeh Pourdarbani *

Garcia-Mateos Gines *

Rubén Fernández-Beltrán

Mohammad Hossein Rohban

DOI：10.3390/app16010303

原文链接

摘要

En 中文

遥感技术在养蜂业中用于姿态估计的应用，有潜力改变蜂群管理，改善蜜蜂健康并缓解蜜蜂数量下降。本文提出了一种新颖的蜜蜂姿态估计方法，该方法整合了两种现有深度学习模型的准确性和效率：一种用于特征提取的经典VGG-19网络架构变体，以及用于部件检测和组装的OpenPose改编版本。所提出的方法OpenBeePose与最先进的方法进行了比较，包括YOLO11和原始OpenPose。所使用的数据集由400张高分辨率蜂箱坡道图像（1080 x 1920像素）组成，这些图像在白天从八个不同的蜂箱中拍摄，总计超过3600个蜜蜂样本。每只蜜蜂均以YOLO格式标注，并标记了两个关键点：螫针和头部。获得的结果表明，OpenBeePose达到了与其他方法相似的高水平准确度，成功率超过99%。然而，其最显著的优势是其计算效率，这使得它在540 x 960图像的测试中成为最快的方法，并且比OpenPose快近两倍。

Keyword:

computer vision

remote sensing in apiculture

deep learning

activity monitoring

期刊

Applied Sciences-Basel

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2.5

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7.6K

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Sharif University of Technology

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1.1W

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被引数: 9.5K

gorgan university of agricultural sciences & natural resources

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1.6K

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被引数: 2

university of murcia

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1.5K

论文数: 659

被引数: 0

University of Mohaghegh Ardabili

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2.5K

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被引数: 2.5K

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