  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.
