Evidence map›Paper›PMID 40994787›Full record

ArticleFrontiers in veterinary science2025

Lameness detection in dairy cows from overhead view: high-precision keypoint localization and multi-feature fusion classification.

Weijun Duan, Fang Wang, Honghui Li, Na Liu, Xueliang Fu

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Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Weijun DuanCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
Fang WangCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
Honghui LiCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
Na LiuNational Centre of Technology Innovation for Dairy-Breeding and Production Research Subcentre, Hohhot, China.
Xueliang FuCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Detecting lameness in dairy cows from an overhead view can effectively avoid occlusion caused by farm facilities or other animals, while suspended detection devices enable parallel monitoring without disturbing natural behaviors. However, existing methods from this perspective still face challenges in accuracy and generalization, largely due to the subtlety of back movement features and individual variability. To address these limitations, this study explores an overhead-view lameness detection approach based on RGB-D data. Methods: We developed a high-precision keypoint detection method for the cow's back that models long-range spatial dependencies and optimizes structural representation. On this basis, six lameness-related features were designed to capture posture and motion abnormalities, including four newly proposed indices. Their correlation in classifying sound, mildly lame, and severely lame cows was systematically analyzed. To further enhance robustness, the Gini importance index from Random Forest combined with a permutation importance correction method (PIMP) was applied to construct an unbiased feature selection framework. Results: Experimental results demonstrate that the proposed keypoint detection network achieved a PCK@0.02 of 100.00% and an average precision of 95.89%, significantly outperforming the baseline model. In feature-based classification, back curvature, movement asymmetry index, and vertical oscillations of the back and head exhibited strong discriminative ability. Using multi-feature fusion, the lameness detection model attained an overall accuracy of 91.00%. Discussion: These findings indicate that overhead RGB-D imaging, combined with precise keypoint detection and feature fusion, provides a reliable strategy for accurate lameness detection in dairy cows. The proposed method offers valuable theoretical and technical support for health monitoring and intelligent management in modern dairy farming.

Indexed as

dairy cowskeypoint detectionlameness detectionmulti-feature fusion classificationoverhead viewRGB-D

Identifiers

PMID40994787
PMCPMC12454108

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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 Socratic graph.