ArticleAnimals : an open access journal from MDPI2022
Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face.
Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 46 citations in OpenAlex.
- Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT.Sensors (Basel, Switzerland) · 2025Article
- YOLO-SDD: An Effective Single-Class Detection Method for Dense Livestock Production.Animals : an open access journal from MDPI · 2025Article
- A deep learning lightweight model for real-time captive macaque facial recognition based on an improved YOLOX model.Zoological research · 2025Article
- Research on herd sheep facial recognition based on multi-dimensional feature information fusion technology in complex environment.Frontiers in veterinary science · 2025Article
- YOLOv8-CBAM: a study of sheep head identification in Ujumqin sheep.Frontiers in veterinary science · 2025Article
- Subclinical Mastitis in Lacaune Sheep: Etiologic Agents, the Effect on Milk Characteristics, and an Evaluation of Infrared Thermography and the YOLO Algorithm as a Preprocessing Tool for Advanced Analysis.Veterinary sciences · 2024Article
- AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.Sensors (Basel, Switzerland) · 2024Article
- A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching.Animals : an open access journal from MDPI · 2024Article
- Lightweight model-based sheep face recognition via face image recording channel.Journal of animal science · 2024Article
- Article
- Lightweight Sheep Head Detection and Dynamic Counting Method Based on Neural Network.Animals : an open access journal from MDPI · 2023Article
- Detection and Localization of Albas Velvet Goats Based on YOLOv4.Animals : an open access journal from MDPI · 2023Article
- SheepFaceNet: A Speed-Accuracy Balanced Model for Sheep Face Recognition.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
- MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing.Sensors (Basel, Switzerland) · 2023Article
- LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization.Animals : an open access journal from MDPI · 2023Article
- A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming.
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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.