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July 1, 2026Concurrency and Computation Practice and Experience

Automatic Cattle Body Measurement via YOLOv11 ‐ RSC and Depth Map Optimization

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Authors

ZWZhi WengLGLei GaoZZZ-Y Zheng

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Overview

Randomized trial demonstrates automated cattle body measurements, indicating improved efficiency in breeding management.

Key Points

  • This research aims to develop an automated system for measuring cattle body size using advanced deep learning techniques to improve efficiency.
  • Developed YOLOv11-RSC framework incorporating a RepViT backbone and multi-scale feature fusion.
  • Deployed the system on edge devices for real-time processing and depth map optimization.
  • Conducted experiments to compare measurements with traditional methods.
  • Achieved a 1.1% increase in box mAP@50 and a 0.8% increase in mask mAP@50 compared to the baseline.
  • Reduced the number of parameters by 24.4% and computational cost by 19.8%.
  • Average relative error for body measurements was less than 6%.

Cite This Study

Weng et al. (2026) studied this question.

synapsesocial.com/papers/6a44af635cd2549c8bc445a1https://doi.org/10.1002/cpe.70848
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1YOLOv8-DMC: Enabling Non-Contact 3D Cattle Body Measurement via Enhanced Keypoint Detection2025 · 6 citations
  2. 2Automated Measurement of Cattle Dimensions Using Improved Keypoint Detection Combined with Unilateral Depth Imaging2024 · 23 citations
  3. 3Automated Measurement of Sheep Body Dimensions via Fusion of YOLOv12n-Seg-SSM and 3D Point Clouds2026 · 1 citations
  4. 4A Lightweight Automatic Cattle Body Measurement Method Based on Keypoint Detection2025
  5. 5An Improved Point Cloud Processing Framework for <scp>3D</scp> Body Measurement of Beef Cattle2026 · 3 citations