| dc.description.abstract | The biggest dilemma faced by dairy farming in Taiwan currently is the lack of labor, especially the problem of cattle management. The current promotion of intelligent farm management aims at accurate management with detection technology. At present, biometric identification mostly uses iris, muzzle, ear tag, etc. as identification targets. However, the above targets all require extremely high image quality, which is difficult in practice. In addition to the maintenance cost, this study intends to use the original scars and facial features of cattle as the identification target.
In this study, 25 cows were used as experimental samples, and a total of 121 images wereused for training and testing. First, YOLOv4 is used to detect, capture and segment feature images of faces and scars, obtain image feature vectors through Triplet network, calculate the Euclidean distance between samples, compare the similarity, and finally get the cattle identification result. The experimental results show that using a single feature as the recognition condition, the recognition rate of cow face is 92%, and the recognition rate of spots is 88%.
Finally, through the method of hybrid neural network, a dual-modal hybrid neural network is designed, and the probability of cattle samples is calculated through the PNN probability neural network for identification, and the recognition rate can reach 96%. Therefore, it is proved that the dual-modal hybrid neural network can effectively improve the biometric identification performance under the few-sample experimental situation. | en_US |