ArticleAnimals : an open access journal from MDPI2023
Cattle Body Detection Based on YOLOv5-EMA for Precision Livestock Farming.
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.
- DGF-YOLOv12n-LiteP2: Dynamic Gated Local-Context Fusion with a High-Resolution Detection Head for Cattle Body-Part Detection in Complex Barns.Sensors (Basel, Switzerland) · 2026Article
- A learning-dynamics-driven framework for robust small-object detection in complex field environments: YOLOv11l-AGRI-EMA framework.Scientific reports · 2026Article
- Advanced 3D Horse Reconstruction: Integrating Image-to-3D Modelling and Non-Rigid Registration for On-Barn Body Measurements.Animals : an open access journal from MDPI · 2026Article
- Image-Based Recognition of Intricate Animal Motifs on Ming Dynasty Blue and White Porcelain Using an Improved YOLOv8n Model.Sensors (Basel, Switzerland) · 2026Article
- UST-YOLO11Pose-TRM: An Attention-Enhanced Keypoint Detection and Transformer Regression Framework for Yak Body Measurement.Animals : an open access journal from MDPI · 2026Article
- Development of a smartphone-based bone maturity classification algorithm with XAI for beef carcass grading.Food science of animal resources · 2026Article
- Spectral-spatial feature fusion for real-time facial expression recognition.Scientific reports · 2025Article
- AI-enhanced infrared thermography for reliable detection and spatial mapping of temperature patterns in calf eyes and muzzles.BMC veterinary research · 2025Article
- Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm.Sensors (Basel, Switzerland) · 2025Article
- A Sheep Behavior Recognition Approach Based on Improved FESS-YOLOv8n Neural Network.Animals : an open access journal from MDPI · 2025Article
- Optimizing black cattle tracking in complex open ranch environments using YOLOv8 embedded multi-camera system.Scientific reports · 2025Article
- Adltformer Team-Training with Detr: Enhancing Cattle Detection in Non-Ideal Lighting Conditions Through Adaptive Image Enhancement.Animals : an open access journal from MDPI · 2024Article
- MS-YOLO: A Lightweight and High-Precision YOLO Model for Drowning Detection.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning-Based Automated Approach for Determination of Pig Carcass Traits.Animals : an open access journal from MDPI · 2024Article
- YOLOv8-MU: An Improved YOLOv8 Underwater Detector Based on a Large Kernel Block and a Multi-Branch Reparameterization Module.Sensors (Basel, Switzerland) · 2024Article
- Research on the Vision-Based Dairy Cow Ear Tag Recognition Method.Sensors (Basel, Switzerland) · 2024Article
- Dynamic Serpentine Convolution with Attention Mechanism Enhancement for Beef Cattle Behavior Recognition.Animals : an open access journal from MDPI · 2024Article
- Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.Cancers · 2024Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Accurate cattle body detection is crucial for precision livestock farming. However, traditional cattle body detection methods rely on manual observation, which is both time-consuming and labor-intensive. Moreover, computer-vision-based methods suffer prolonged training times and training difficulties. To address these issues, this paper proposes a novel YOLOv5-EMA model for accurate cattle body detection. By incorporating the Efficient Multi-Scale Attention (EMA) module into the backbone of YOLO series detection models, the performance of detecting smaller targets, such as heads and legs, has been significantly improved. The Efficient Multi-Scale Attention (EMA) module utilizes the large receptive fields of parallel sub-networks to gather multi-scale spatial information and establishes mutual dependencies between different spatial positions, enabling cross-spatial learning. This enhancement empowers the model to gather and integrate more comprehensive feature information, thereby improving the effectiveness of cattle body detection. The experimental results confirm the good performance of the YOLOv5-EMA model, showcasing promising results across all quantitative evaluation metrics, qualitative detection findings, and visualized Grad-CAM heatmaps. To be specific, the YOLOv5-EMA model achieves an average precision (mAP@0.5) of 95.1% in cattle body detection, 94.8% in individual cattle detection, 94.8% in leg detection, and 95.5% in head detection. Moreover, this model facilitates the efficient and precise detection of individual cattle and essential body parts in complex scenarios, especially when dealing with small targets and occlusions, significantly advancing the field of precision livestock farming.
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