Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM

•A method for individual identification of cows in unconstrained barn was developed.•The extracted and selected features have strong adaptability to pattern deformation.•A method involving fusion of Mask R-CNN and SVM was used to identify cows.•To reduce the running time and number of model parameters, Mask R-CNN was modified. The identification of individual dairy cows is an important prerequisite for dairy cow behaviour analysis and disease detection. Computer vision-based cow recognition is a noncontact and stress-free approach. In a free environment in a barn, due to changes in camera position and angle, recorded cow patterns are often deformed, making individual cow identification difficult. For cows in an unconstrained barn environment, this paper proposes a method for individual cow identification. First, a top-view image of a cow is obtained, and an improved Mask R-CNN is used to segment this image and extract the shape features of the cow’s back. Then, a Fisher approach is used to select the best feature subset, and a support vector machine (SVM) classifier is applied to identify individual cows. To verify the effectiveness of the target detection algorithm, the proposed method is compared with the traditional Mask R-CNN model, and the precision, recall, F1 score, average run time per image and average precision of the improved Mask R-CNN model are 98.21%, 96.48%, 97.34%, 1.02 s, and 97.39%, respectively. An SVM classifier trained based on the obtained shape features is used for individual cow identification. The proposed method achieves a 98.67% cow identification accuracy based on a dataset containing top-view images of 48 cows. The results demonstrate the effectiveness of the proposed cow identification method and its significant potential for use in precision dairy cow management.