Исследовательская база

Исследовательские статьи 2021 года

Статьи, датасеты, модели и полевые заметки собраны в визуальную карту знаний, сохраняя глубокую структуру раздела.

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Акустика и звук

Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring — 🇺🇸 Utah State University
Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths — 🇺🇸 X, the Moonshot Factory; 🇺🇸 Caltech; 🇺🇸 Google; 🇺🇸 University of Wisconsin-Madison; 🇺🇸 Washington State University

Поведение пчёл

BeePIV: A Method to Measure Apis Mellifera Traffic with Particle Image Velocimetry in Videos — 🇺🇸 Utah State University
DeepLabCut-based daily behavioural and posture analysis in a cricket — 🇯🇵 Waseda University
Honeybee In-Out Monitoring System by Object Recognition and Tracking from Real-Time Webcams — 🇰🇷 Incheon National University
Markerless tracking of an entire honey bee colony — 🇯🇵 Okinawa Institute of Science and Technology Graduate University; 🇦🇺 Australian National University; 🇳🇱 Vrije Universiteit Amsterdam
On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks — 🇺🇸 Utah State University
Social networks predict the life and death of honeybees — 🇩🇪 Freie Universität Berlin; 🇩🇪 Robert Koch Institute; 🇩🇪 University of Konstanz; 🇺🇸 Auburn University; 🇺🇸 University of Hohenheim

Компьютерное зрение

BeePIV: A Method to Measure Apis Mellifera Traffic with Particle Image Velocimetry in Videos — 🇺🇸 Utah State University
DeepLabCut-based daily behavioural and posture analysis in a cricket — 🇯🇵 Waseda University
Honeybee In-Out Monitoring System by Object Recognition and Tracking from Real-Time Webcams — 🇰🇷 Incheon National University
Markerless tracking of an entire honey bee colony — 🇯🇵 Okinawa Institute of Science and Technology Graduate University; 🇦🇺 Australian National University; 🇳🇱 Vrije Universiteit Amsterdam
On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks — 🇺🇸 Utah State University
Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques — 🇨🇿 Brno University of Technology

Датасеты и бенчмарки

Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring — 🇺🇸 Utah State University

Edge AI и энергопотребление

Self-Powered Smart Beehive Monitoring and Control System (SBMaCS) — 🇷🇼 University of Rwanda; 🇿🇲 Copperbelt University; 🇷🇼 National Council of Science and Technology

IoT и сенсоры

Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring — 🇺🇸 Utah State University
Self-Powered Smart Beehive Monitoring and Control System (SBMaCS) — 🇷🇼 University of Rwanda; 🇿🇲 Copperbelt University; 🇷🇼 National Council of Science and Technology
Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths — 🇺🇸 X, the Moonshot Factory; 🇺🇸 Caltech; 🇺🇸 Google; 🇺🇸 University of Wisconsin-Madison; 🇺🇸 Washington State University
Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services — 🇫🇷 aivancity School for Technology; 🇫🇷 Université de Lyon

Обзоры и систематические обзоры

Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services — 🇫🇷 aivancity School for Technology; 🇫🇷 Université de Lyon

Варроа и здоровье

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques — 🇨🇿 Brno University of Technology