Abstract: Non-contact method for cattle individual identification has advantages in cost reduction of identification,simplification of identification process and accurate identification,which has been fully developed in cattle individual identification in recent years.There are some problems in existing research,such as recognition accuracy affected by external factors(environment,weather,etc.) too much and difficult transfer learning.In view of the above problems,a cattle individual identification model with 3 modules based on YOLO-Unet combined network is proposed.Firstly,the image extraction module is constructed by YOLOv5 model to extract cattle facial images.Then,the background removal module is constructed by Unet model to remove the background of cattle facial images for eliminating the environmental impact,so as to improve the model’s generalization ability.Finally,the individual classification module is constructed by MobileNetV3 model to classify the cattle facial images whose background removed.Ablation experiment is performed on the background elimination module.The experimental result shows that the background removal module can greatly improve the model’s generalization ability.The recognition accuracy of the model with background removal module is 90.48%,which is 11.99％ higher than that of the model without background removal module.

Key words: Cattle individual identification, Deep learning, Object detection, Semantic segmentation, Object recognition, Generalization Ability