Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector

Abstract

Intelligent lameness detection is important for improving cow welfare. A method based on YOLOv3 deep learning algorithm and relative step size characteristic vector is proposed to classify lame and non-lame cows. Videos were decomposed into sequence frames, and leg targets of cows in each frame were detected by YOLOv3 algorithm. Relative step sizes of cow's front and rear legs were calculated based on leg coordinates, and the relative step size characteristic vector was constructed. Finally, a trained Long Short-Term Memory (LSTM) classification model was used to classify lame and non-lame cows based on the characteristic vector. A total of 210 videos were selected for verification using LSTM, support vector machine (SVM), K-Nearest Neighbour (KNN) and decision tree classifier (DTC) algorithms. Results showed that accuracy of lameness detection based on LSTM was 98.57%, which was 2.93%, 3.88%, and 9.25% higher than SVM, KNN, and DTC, respectively. True positive rate of the LSTM was 0.97, which was 0.03, 0.04 and 0.06 higher than SVM, KNN and DTC, respectively. False positive rate of LSTM was 0.03, which was 0.03, 0.06 and 0.11 lower than SVM, KNN and DTC, respectively. A bidirectional LSTM performed slightly better than LSTM but would be more demanding on hardware. Comparison of LSTM with a purely deep learning method showed the latter performed slightly better, but was less conducive to interpretation and diagnosis of lameness. The relative step size characteristic vector proposed was effective for classification of lame and non-lame cows, and could lead to intelligent detection of lameness.