ArticleAnimals : an open access journal from MDPI2021
Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes.
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 33 citations in OpenAlex.
- Amur Tiger Individual Identification Based on the Improved InceptionResNetV2.Animals : an open access journal from MDPI · 2024Article
- Improved Chinese Giant Salamander Parental Care Behavior Detection Based on YOLOv8.Animals : an open access journal from MDPI · 2024Article
- Detection of Pine Wilt Nematode from Drone Images Using UAV.Sensors (Basel, Switzerland) · 2022Article
- Development of a Slow Loris Computer Vision Detection Model.Animals : an open access journal from MDPI · 2022Article
- Postural behavior recognition of captive nocturnal animals based on deep learning: a case study of Bengal slow loris.Scientific reports · 2022Article
- Computer Vision for Detection of Body Posture and Behavior of Red Foxes.Animals : an open access journal from MDPI · 2022Article
- Baseline of Physiological Body Temperature and Hematological Parameters in CaptiveFrontiers in physiology · 2022Article
- Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm 'you only look once' version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.