Research On Fast Recognition Algorithm Of Cow Face Based On Mobile Terminal

As a breeding base of animal husbandry and an important production base of beef and mutton in China,Inner Mongolia undertakes the heavy task of supplying beef products throughout the country.However,in the face of a great number of cattle,an efficient production and management mechanism has been not established.The identification of cattle is the basics of establishing the management mechanism.At present,the cattle’s identification and management are realized by wearing radiofrequency tag on the ears of cattle.However,this invasive method is easy to cause these side effects such as cattle’s discomfort,biting the label and dropping the label.Traditional methods are time-consuming and inefficient.With the emergencing of artificial intelligence,the contactless and intelligent cow face recognition has become one of the research hotspots.In this paper,cattle face images collected by the camera are taken as the research object.With the help of popular deep learning and face recognition technology,cow face detection and recognition experiments are carried out.In addition,a cow face recognition system based on mobile terminal is designed to provide assistance for subsequent intelligent breeding,which has certain practical significance.In view of the cow face images,this paper mainly studies:(1)Based on cow face detection algorithm,an efficient and accurate cow face detection model is built.In order to obtain a cow face detection model with strong generalization ability.Firstly,a Multi-Task Convolutional Neural Network(MTCNN)is used as the basis,in order to compress the amount of computation and parameters of Convolutional Neural Networks(CNN),a deep separable convolutional structure is introduced to replace the original standard convolutional structure.Secondly,in order to improve the accuracy and recall rate of the model,multi-scale convolution kernel and attention mechanism are introduced.Finally,the improved MTCNN network model is trained,and the improved model is applied to cow face detection.The results show that the accuracy rate of single bull face image detection reaches 97.3%,which is 3.1percentage points higher than that of the improved MTCNN network.The detection speed is 16.5 frames per second,which is 3.4 frames faster than that of the improved network.(2)Based on cow face recognition algorithm,a fast recognition model based on cow face image is designed.Firstly,the original features of the cow faces were mapped to a low-dimensional space by using the triple loss function,which made the intra-class differences of the pictures of cows with the same ID smaller,and the differences between classes of cows with different IDs larger.Secondly,Mobile Net,a lightweight neural network with low computational complexity and small number of parameters,is used for training to facilitate the deployment of mobile terminals.Finally,the experimental results show that the recognition accuracy of the lightweight model using Triplet loss function combined with Mobile Net on mobile terminal reaches 95.4%.(3)Based on Android mobile terminal,cow face detection and recognition function is realized.Firstly,the idea of cow face recognition system is introduced.Secondly,the process of bull face recognition on mobile terminal is analyzed.Then,the system development process to do a simple description.Finally,the effects of cow face detection and cow face recognition are demonstrated.