Research on dairy cow identification methods in dairy farm Bo Liu1, Yuefeng Liu1,*, Xiang Bao1, Yue Wang1, Haofeng Liu1, Xuan Long2 1School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China 2School of Economics and Management, Southeast University, Nanjing, 211189, China *Corresponding Author: Liu Yuefeng, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China, Email: 15947325205@163.com
Online Published on 5 January, 2024. Abstract Background There is a large amount of occlusion in dairy farms and changes in daytime and nighttime lighting seriously affect the accuracy of traditional dairy cow identification. Methods This paper proposed a method to dairy cow identification in dairy farm. Firstly, Resnet 50 was used to extract the pattern features of dairy cows and this paper fused the fourth and fifth scale features with semantic information. Secondly, on the basis of the triplet loss, it added label smoothing loss instead of softmax cross-entropy loss to further avoid over-fitting and reduced the intra-class distance with the center loss, which improved the mean average precision (mAP) by 8.9% compared with the initial Re-identification (Reid) and the joint optimized distance calculation formula was improved the single distance measurement in the triplet loss, using two distance measurement methods to effectively improve the recognition accuracy. Finally, it was combined with pruning operation to reduce the redundant parameters of the model. Result The experiment proves that the identification mAP of dairy cows reaches 81.2%, which is an improvement 10.9% compared to the baseline model mAP, providing a strong technical support for the subsequent dairy cow identification and target tracking system. Top Keywords Feature fusion, Image recognition, Metric learning, Model compression, Object detection. Top |