Intelligent Deep Learning and Keypoint Tracking-Based Detection of Lameness in Dairy Cows

Abstract

With the ongoing development of computer vision technologies, the automation of lameness detection in dairy cows urgently requires improvement. To address the challenges of detection difficulties and technological limitations, this paper proposes an automated scoring method for cow lameness that integrates deep learning with keypoint tracking. First, the DeepLabCut tool is used to efficiently extract keypoint features during the walking process of dairy cows, which enables the automated monitoring and output of positional information. Then, the extracted positional data are combined with temporal data to construct a scoring model for cow lameness. The experimental results demonstrate that the proposed method tracks the keypoint of cow movement accurately in visible-light videos and satisfies the requirements for real-time detection. The model classifies the walking states of the cows into four levels, i.e., normal, mild, moderate, and severe lameness (corresponding to scores of 0, 1, 2, and 3, respectively). The detection results obtained in real-world real environments exhibit the high extraction accuracy of the keypoint positional information, with an average error of only 4.679 pixels and an overall accuracy of 90.21%. The detection accuracy for normal cows was 89.0%, with 85.3% for mild lameness, 92.6% for moderate lameness, and 100.0% for severe lameness. These results demonstrate that the application of keypoint detection technology for the automated scoring of lameness provides an effective solution for intelligent dairy management.

Keywords

• dairy cows
• machine learning
• keypoints
• feature extraction
• lameness