Video-based automatic lameness detection of dairy cows using pose estimation and multiple locomotion traits
This addresses lameness detection in dairy cows, an important welfare issue in agriculture, with an incremental improvement in accuracy through multi-trait analysis.
The study tackled automated lameness detection in dairy cows by using pose estimation to extract multiple locomotion traits from videos, achieving a classification accuracy of up to 80.1% with all six traits, compared to 76.6% with a single trait.
This study presents an automated lameness detection system that uses deep-learning image processing techniques to extract multiple locomotion traits associated with lameness. Using the T-LEAP pose estimation model, the motion of nine keypoints was extracted from videos of walking cows. The videos were recorded outdoors, with varying illumination conditions, and T-LEAP extracted 99.6% of correct keypoints. The trajectories of the keypoints were then used to compute six locomotion traits: back posture measurement, head bobbing, tracking distance, stride length, stance duration, and swing duration. The three most important traits were back posture measurement, head bobbing, and tracking distance. For the ground truth, we showed that a thoughtful merging of the scores of the observers could improve intra-observer reliability and agreement. We showed that including multiple locomotion traits improves the classification accuracy from 76.6% with only one trait to 79.9% with the three most important traits and to 80.1% with all six locomotion traits.