Lameness detection system for dairy cows based on instance segmentation

Abstract

Lameness is one of the major health problems on dairy farms, which seriously affects dairy cow welfare and increases the risk of premature culling. Accuracy lameness detection ensures timely treatment of hooves and improves the level of health management on dairy farms. Most of the existing lameness detection methods detect one dairy cow, and it is difficult to detect lameness in multiple dairy cows at the same time. In this paper, a lameness detection system based on instance segmentation is constructed to extract the lameness features of multiple dairy cows and automatically detect lameness. First, an improved SOLOv2 network is designed to reduce the semantic gap between low-level and high-level features and improve the precision of dairy cow segmentation. Second, individual matching of dairy cows in different video frames is performed using the Hungarian algorithm. Third, the Canny algorithm is used to extract the neck and back contour features and key gait features of dairy cows. Finally, a deep learning model is constructed, and multiple lameness features are fused to detect the lameness of dairy cows. To evaluate the performance of the constructed system, 172 videos were randomly selected from 246 videos as training videos, and the remaining 74 videos were selected as the test videos. The lameness detection accuracy of the constructed system was 98.65%. The experimental results showed that the constructed system can extract the lameness features of multiple dairy cows at the same time and accurately detect the lameness of dairy cows.