ArticleTranslational animal science2024
Deep learning based landmark detection for measuring hock and knee angles in sows.
Article in Translational animal science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Dog facial landmarks detection and its applications for facial analysis.Scientific reports · 2025Article
- Monitoring Pig Structural Soundness and Body Weight in Pork Production Systems Using Computer Vision Approaches.Animals : an open access journal from MDPI · 2025Review
- Genetic parameters for image-based estimations of swine feet and leg conformation traits.Journal of animal science · 2025Article
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Authors and funding
5 authors.
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Abstract
This paper presents a visual deep learning approach to automatically determine hock and knee angles from sow images. Lameness is the second largest reason for culling of breeding herd females and relies on human observers to provide visual scoring for detection which can be slow, subjective, and inconsistent. A deep learning model classified and detected ten and two key body landmarks from the side and rear profile images, respectively (mean average precision = 0.94). Trigonometric-based formulae were derived to calculate hock and knee angles using the features extracted from the imagery. Automated angle measurements were compared with manual results from each image (average root mean square error [RMSE] = 4.13°), where all correlation slopes (average
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