OpenBeePose: A Remote Sensing Framework for Bee Pose Estimation Using Deep Learning
Source abstract
Abstract
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 × 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 × 960 images, and it is almost twice as fast as OpenPose.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Insect and Arachnid Ecology and Behavior · Insect and Pesticide Research · Neurobiology and Insect Physiology Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49138-15739, Iran
Department of Computer Engineering, Sharif University of Technology, Tehran 14588-89694, Iran
Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain
Funding
Regional Program for the Promotion of Scientific and Technical Research of Excellence (Action Plan 2022) of the Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia, Spain
References (38)
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