Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video
This addresses early lameness detection for dairy farmers to reduce economic losses, but it is incremental as it builds on existing segmentation and feature extraction techniques.
The paper tackles cow lameness detection by proposing a method that uses binary segmentation masks from RGB and depth videos to focus on cow structure, achieving a binary classification of 'healthy' or 'lame'.
Cow lameness is a severe condition that affects the life cycle and life quality of dairy cows and results in considerable economic losses. Early lameness detection helps farmers address illnesses early and avoid negative effects caused by the degeneration of cows' condition. We collected a dataset of short clips of cows passing through a hallway exiting a milking station and annotated the degree of lameness of the cows. This paper explores the resulting dataset and provides a detailed description of the data collection process. Additionally, we proposed a lameness detection method that leverages pre-trained neural networks to extract discriminative features from videos and assign a binary score to each cow indicating its condition: "healthy" or "lame." We improve this approach by forcing the model to focus on the structure of the cow, which we achieve by substituting the RGB videos with binary segmentation masks predicted with a trained segmentation model. This work aims to encourage research and provide insights into the applicability of computer vision models for cow lameness detection on farms.