Sheep face image dataset and DT-YOLOv5s for sheep breed recognition

Abstract

Automatic identification of breeds of sheep can be valuable to the sheep industry. Due to the lack of open source datasets of multiple breeds in some researches, which leads to poor robustness of models for automatic sheep breed recognition. This paper proposes a sheep face image dataset of 10 breeds, which consists of 5200 original images of 823 sheep from seven farms in Inner Mongolia. The dataset is expanded to 44,747 images after data enhancement. A sheep breed recognition model DT-YOLOv5s based on knowledge distillation is proposed for the dataset. The model transfers the knowledge of sheep face features learned by the teacher network YOLOv5x, which has a large number of parameters but high recognition accuracy, to the lightweight student network YOLOv5s through a 15-dimensional high-dimensional semantic feature vector, so as to improve the student network's PANet feature extraction capability and recognition accuracy, achieving the purpose of lightweight and accurate recognition of the overall network model. The experimental results show that the accuracy of our proposed model is 92.75%, which is 11.19%, 13.1% and 13.65% higher than that of SSD, Faster R-CNN and YOLOv5s models, respectively. The mean average precision (mAP@[0.5:0.95]) is 94.67%, which is 7.85%, 2.35% and 4.83% higher than that of SSD, Faster R-CNN and YOLOv5s models, respectively. Meanwhile, the inference time of the model is 12.63 ms, which is 33.9 ms, 53.13 ms and 74.61 ms less than SSD, Faster R-CNN and YOLOv5x, respectively. The proposed DT-YOLOv5s model in this paper has good robustness and generalization ability, which can provide technical support for the real-time accurate identification of the sheep breed for farmers.