ArticleAnimals : an open access journal from MDPI2023
LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End.
Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
- YOLO-WL: A Lightweight and Efficient Framework for UAV-Based Wildlife Detection.Sensors (Basel, Switzerland) · 2026Article
- Article
- EnhancedMulti-Scenario Pig Behavior Recognition Based on YOLOv8n.Animals : an open access journal from MDPI · 2025Article
- Aging-Invariant Sheep Face Recognition Through Feature Decoupling.Animals : an open access journal from MDPI · 2025Article
- Lightweight Sheep Face Recognition Model Combining Grouped Convolution and Parameter Fusion.Sensors (Basel, Switzerland) · 2025Article
- A Novel Lightweight Framework for Non-Contact Broiler Face Identification in Intensive Farming.Sensors (Basel, Switzerland) · 2025Article
- YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation.Sensors (Basel, Switzerland) · 2025Article
- A Review of You Only Look Once Algorithms in Animal Phenotyping Applications.Animals : an open access journal from MDPI · 2025Review
- Pig Face Open Set Recognition and Registration Using a Decoupled Detection System and Dual-Loss Vision Transformer.Animals : an open access journal from MDPI · 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
- Efficient Cow Body Condition Scoring Using BCS-YOLO: A Lightweight, Knowledge Distillation-Based Method.Animals : an open access journal from MDPI · 2024Article
- A Deep-Learning-Based CPR Action Standardization Method.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
- An improved YOLOv8n-IRP model for natural rubber tree tapping surface detection and tapping key point positioning.Frontiers in plant science · 2024Article
- Detection and Localization of Albas Velvet Goats Based on YOLOv4.Animals : an open access journal from MDPI · 2023Article
- Contextualized Small Target Detection Network for Small Target Goat Face Detection.Animals : an open access journal from MDPI · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.
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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.