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.
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.
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.
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.
Feature fusion, Image recognition, Metric learning, Model compression, Object detection
