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OpenBeePose: A Remote Sensing Framework for Bee Pose Estimation Using Deep Learning
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DOI：10.3390/app16010303.png)
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Abstract
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The application of remote sensing technology for pose estimation in beekeeping has the potential to transform colony management, improve bee health and mitigate the decline in bee populations. This paper presents a novel bee pose estimation method that integrates the accuracy and efficiency of two existing deep learning models: a variant of the classic VGG-19 network architecture for feature extraction and an adaptation of OpenPose for part detection and assembly. The proposed approach, OpenBeePose, is compared with state-of-the-art methods, including YOLO11 and the original OpenPose. The dataset used consists of 400 high-resolution images of the hive ramp (1080 x 1920 pixels) taken during daylight hours from eight different hives, totaling more than 3600 bee samples. Each bee is annotated in the YOLO format with two key points labeled: the stinger and the head. The obtained results show that OpenBeePose achieves a high level of accuracy, similar to those of other methods, with a success rate exceeding 99%. However, the most substantial advantage is its computational efficiency, which makes it the fastest method among those compared for 540 x 960 images, and it is almost twice as fast as OpenPose.
Keywords:
computer vision
remote sensing in apiculture
deep learning
activity monitoring
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2.5
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