Исследовательская продуктовая область: Hive Scanner

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M3DANet: A Lightweight Semi-Supervised Network and Embedded System for Bee Colony Counting — 🇨🇳 Shandong Agricultural University; 🇨🇳 Apiculture Institute of Jiangxi Province

An AI-Based Digital Scanner for Varroa destructor Detection in Beekeeping — 🇮🇹 Council for Agricultural Research and Economics (CREA) – Research Centre for Engineering and Agro-Food Processing, Monterotondo; 🇮🇹 Council for Agricultural Research and Economics (CREA) – Research Centre for Agriculture and Environment, Bologna; 🇷🇸 University of Novi Sad
Deep Learning-Based Detection of Honey Storage Areas in Apismellifera Colonies for Predicting Physical Parameters of Honey via Linear Regression — 🇹🇭 Chiang Mai University
Fast, accurate measurement of the worker populations of honey bee colonies using deep learning — 🇺🇸 Arizona State University; 🇺🇸 Texas A&M University–Kingsville

SLEAP: A deep learning system for multi-animal pose tracking — 🇺🇸 Princeton University; 🇺🇸 New York University; 🇺🇸 Johns Hopkins University School of Medicine

DeepLabCut-based daily behavioural and posture analysis in a cricket — 🇯🇵 Waseda University
Markerless tracking of an entire honey bee colony — 🇯🇵 Okinawa Institute of Science and Technology Graduate University; 🇦🇺 Australian National University; 🇳🇱 Vrije Universiteit Amsterdam

Automatic detection and classification of honey bee comb cells using deep learning — 🇵🇹 Instituto Politécnico de Bragança; 🇧🇷 Federal Technological University of Paraná; 🇫🇷 Université Clermont-Auvergne
Real-time, low-latency closed-loop feedback using markerless posture tracking — 🇺🇸 Harvard University; 🇬🇧 NeuroGEARS Ltd; 🇺🇸 University of Oregon; 🇨🇭 Swiss Federal Institute of Technology

DeepBees – Building and Scaling Convolutional Neuronal Nets For Fast and Large-scale Visual Monitoring of Bee Hives — 🇩🇪 Karlsruhe Institute of Technology; 🇩🇪 apic.ai
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning — 🇩🇪 Max Planck Institute of Animal Behavior; 🇩🇪 University of Konstanz; 🇺🇸 Princeton University; 🇩🇪 Technische Universität München

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning — 🇩🇪 Eberhard Karls Universität Tübingen; 🇺🇸 Harvard University; 🇺🇸 Columbia University; 🇩🇪 Max Planck Institute for Biological Cybernetics; 🇩🇪 Bernstein Center for Computational Neuroscience; 🇺🇸 Baylor College of Medicine
Multiple Animals Tracking in VideoUsing Part Affinity Fields — 🇵🇷 University of Puerto Rico
The development of honey bee coloniesassessed using a new semi-automated broodcounting method: CombCount — 🇦🇺 Macquarie University; 🇦🇺 Queensland University of Technology; 🇺🇸 Carl Hayden Bee Research Center
Towards dense object tracking in a 2D honeybee hive — 🇯🇵 Okinawa Institute of Science and Technology Graduate University; 🇳🇱 VU University Amsterdam
Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence — 🇩🇪 Freie Universität Berlin; 🇩🇪 University of Konstanz; 🇩🇪 Robert Koch Institute; 🇺🇸 Auburn University; 🇺🇸 University of Hohenheim