摘要
为了在牦牛养殖过程中对牦牛实现精确管理,需要对牦牛的身份进行识别,而牦牛脸识别是一种可行的牦牛身份识别方式。然而已有的基于神经网络的牦牛脸识别算法中存在牦牛脸数据集特征多、神经网络训练时间长的问题,因此,借鉴迁移学习的方法并结合视觉几何组网络(VGG)和卷积神经网络(CNN),提出了一种并行CNN(Parallel-CNN)算法用来识别牦牛的面部信息。首先,利用已有的VGG16网络对牦牛脸图像数据进行迁移学习以及初次提取牦牛的面部信息特征;然后,将提取到的不同层次的特征进行维度变换并输入到Parallel-CNN中进行二次特征提取;最后,利用两个分离的全连接层对牦牛脸图像进行分类。实验结果表明:Parallel-CNN能够对不同角度、光照和姿态的牦牛脸进行识别,在含有300头牦牛的90 000张牦牛脸图像的测试数据集上,所提算法的识别准确率达到91.2%。所提算法可以对牦牛身份进行精确识别,从而帮助牦牛养殖场实现对牦牛的智能化管理。
In order to realize accurate management of yaks during the process of yak breeding,it is necessary to recognize the identities of the yaks.Yak face recognition is a feasible method of yak identification.However,the existing yak face recognition algorithms based on neural networks have the problems such as too many features in the yak face dataset and long training time of neural networks.Therefore,based on the method of transfer learning and combined with the Visual Geometry Group(VGG)network and Convolutional Neural Network(CNN),a Parallel CNN(Parallel-CNN)algorithm was proposed to identify the facial information of yaks.Firstly,the existing VGG16 network was used to perform transfer learning to the yak face image data and extract the yaks’facial information features for the first time.Then,the dimensional transformation was performed to the extracted features at different levels,and the processed features were inputted into the parallel-CNN for the secondary feature extraction.Finally,two separated fully connected layers were used to classify the yak face images.Experimental results showed that Parallel-CNN was able to recognize yak faces with different angles,illuminations and poses.On the test dataset with 90000 yak face images of 300 yaks,the recognition accuracy of the proposed algorithm reached 91.2%.The proposed algorithm can accurately recognize the identities of the yaks,and can help the yak farm to realize the intelligent management of the yaks.
作者
陈争涛
黄灿
杨波
赵立
廖勇
CHEN Zhengtao;HUANG Can;YANG Bo;ZHAO Li;LIAO Yong(Sichuan State-owned Assets Investment and Management Company Limited,Chengdu Sichuan 610031,China;Sichuan SDIC Modern Agriculture and Animal Husbandry Industry Company Limited,Chengdu Sichuan 610041,China;Chengdu Simu-Tech Science and Technology Development Company Limited,Chengdu Sichuan 610041,China;School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China)
出处
《计算机应用》
CSCD
北大核心
2021年第5期1332-1336,共5页
journal of Computer Applications
基金
四川省高原生态产业发展研究中心2020年度科研项目(YJZX-2020-2)。
关键词
牦牛脸识别
深度学习
迁移学习
卷积神经网络
并行网络
yak face recognition
deep learning
transfer learning
Convolutional Neural Network(CNN)
parallel network
