Object Detection for Cattle Gait Tracking

Abstract

Lameness in cattle is a health issue where gait is modified to minimise pain. Cattle are currently visually assessed for locomotion score, which provides the degree of lameness for individual animals. This subjective method is costly in terms of labour, and its level of accuracy and ability to detect small changes in locomotion that is critical for early detection of lameness and associated intervention. Current automatic lameness detection systems found in literature have not yet met the ultimate goal of widespread commercial adoption. We present a sensor configuration to record cattle kinematics towards automatic lameness detection. This configuration features four Time of Flight sensors to view cattle from above and from one side as they exit an automatic rotary milking dairy. Two dimensional near infrared images sampled from 223 cows passing through the system were used to train a Faster R-CNN to detect hooves (F1-score = 0.90) and carpal/tarsal joints (Fl-score = 0.85). The depth images were used to project these detected key points into Cartesian space where they were tracked to obtain individual trajectories per limb. The results show that kinematic gait features can be successfully obtained as a first and important step towards objective, accurate, automatic lameness detection.