ArticleEcology and evolution2019
Wildlife surveillance using deep learning methods.
Article in Ecology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- An Improved Lightweight Model for Protected Wildlife Detection in Camera Trap Images.Sensors (Basel, Switzerland) · 2025Article
- YOLOv10n-CF-Lite: A Method for Individual Face Recognition of Hu Sheep Based on Automated Annotation and Transfer Learning.Animals : an open access journal from MDPI · 2025Article
- A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards.Frontiers in artificial intelligence · 2025Article
- Review
- An Improved Bird Detection Method Using Surveillance Videos from Poyang Lake Based on YOLOv8.Animals : an open access journal from MDPI · 2024Article
- Improved Re-Parameterized Convolution for Wildlife Detection in Neighboring Regions of Southwest China.Animals : an open access journal from MDPI · 2024Article
- Lightweight model-based sheep face recognition via face image recording channel.Journal of animal science · 2024Article
- A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s.Animals : an open access journal from MDPI · 2023Article
- Optimized Small Waterbird Detection Method Using Surveillance Videos Based on YOLOv7.Animals : an open access journal from MDPI · 2023Article
- LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End.Animals : an open access journal from MDPI · 2023Article
- Animal Species Recognition with Deep Convolutional Neural Networks from Ecological Camera Trap Images.Animals : an open access journal from MDPI · 2023Article
- An overview of remote monitoring methods in biodiversity conservation.Environmental science and pollution research international · 2022Review
- Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face.Animals : an open access journal from MDPI · 2022Article
- AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment.Sensors (Basel, Switzerland) · 2021Article
- UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices.Sensors (Basel, Switzerland) · 2020Article
- Wildlife surveillance using deep learning methods.Ecology and evolution · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wildlife conservation and the management of human-wildlife conflicts require cost-effective methods of monitoring wild animal behavior. Still and video camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. In the present study, we describe a state-of-the-art, deep learning approach for automatically identifying and isolating species-specific activity from still images and video data.We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species (binary classification) and secondly to distinguish each of six animal species (multiclassification). We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage
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Registered trials
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