联合损失优化下的高相似度奶山羊身份识别  

Joint loss optimization based high similarity identification for milch goats

作　　者：尚诚 王美丽[1,2,3] 宁纪锋 李群辉[4] 姜雨[5] 王小龙[5] Shang Cheng;Wang Meili;Ning Jifeng;Li Qunhui;Jiang Yu;Wang Xiaolong(College of Information Engineering,Northwest A&F University,Yangling 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling 712100,China;Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service,Yangling 712100,China;School of Science,Chang′an University,Xi′an 710064,China;College of Animal Science and Technology,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学信息工程学院,杨凌712100 [2]农业农村部农业物联网重点实验室,杨凌712100 [3]陕西省农业信息与智能服务重点实验室,杨凌712100 [4]长安大学理学院,西安710064 [5]西北农林科技大学动物科技学院,杨凌712100

出　　处：《中国图象图形学报》2022年第4期1137-1147,共11页Journal of Image and Graphics

基　　金：陕西省重点研发项目(2022QFY11-03);农村农业部农业物联网重点实验室项目(2018AIOT-09);陕西省农业科技创新转化项目(NYKJ-2020-YL-07);陕西省农业科技创新驱动项目(NYKJ-2021-YL(XN)48)。

摘　　要：目的动物个体身份识别一直是智慧畜牧业的主要难题之一,由于动物个体本身与人类在图像识别上需要的数据特征不同以及各个特征作为个体属性之间的关系不明确,对动物个体识别领域的研究较少,针对具有高相似度的奶山羊个体身份识别问题,提出了基于深度学习的高相似度的奶山羊识别方法。方法采集了26只萨能奶山羊的全身图像,利用SSD(single shot MultiBox detection)网络进行数据集预处理,并随机选取1040幅图像作为训练集,260幅图像作为测试集。其次采用Res Net18(residual neural network)预训练模型并进行迁移学习,最后联合三元组损失函数与交叉熵损失函数进行参数调整。研究表明,采用联合损失函数并结合Adam优化器算法时,可获得较好的识别效果。此外,在实验部分针对奶山羊的特征选取问题上,对奶山羊的羊脸区域与奶山羊的全身区域分别采用了三元组损失函数与孪生网络,验证了对奶山羊的识别仅靠羊脸区域的特征时准确率较低;此外,针对网络的训练,本文不仅通过YOLOv3(you only look once)以及孪生网络(siamese network)验证了奶山羊本身属于高相似度的数据集,而且针对奶山羊数据集分别采用三元组损失函数与交叉熵损失函数作为唯一的损失函数,并验证了该方法的有效性。结果奶山羊识别的最高精准度为93.077%,相较于Triplet-Loss损失函数74.615%的准确率以及Cross Entropy-Loss 89.615%准确率有了较大提升。结论本文提出的基于深度学习的高相似度的奶山羊识别方法不仅具有较高的准确率,而且在奶山羊个体身份识别方面具有极大的应用价值,有助于准确识别羊的身份,为相似度高的动物个体身份识别提供了思路。Objective It is essential for the quick response in tracking information of animals for intelligent agriculture and animal husbandry nowadays.Individual identification of animals has been one of the challenging issues in real-time monitoring.Different from traditional methods with high harmfulness such as imprinting,our deep learning based method is adopted to implement image recognition for the several of animal and human as well as the unclear multi-features relationship.Method First,our computer vision method is demonstrated for individual recognition of dairy goat based on deep learning.The 26 goats’pictures-oriented are acquired including the head and other parts.Fancy fancy principal components analysis(PCA)is adopted as the data expansion methods to expand the dataset.A sum of 1040 goats’images are randomly selected for training,and 260 images are used as independent test sets;single shot MultiBox detection(SSD)network based dataset preprocessing is initial to be required.Our demonstration uses the siamese network for preliminary learning.The network structure and learning rate optimization algorithm are employed to adjust the parameters but not suitable for individual identity classification.It verifies the goat itself in terms of the highly similar data set.The effect of whole goat image is better than single head image.This obtained result has been greatly improved from the training of original head to the whole body in the context of is the solo Triplet-Loss function.The original image input of the Triplet-Loss function is composed of three pictures.Because the dairy goat is proven to have high similarity of individual based on the siamese network,it is not required to conduct the data sets integration derived of the Triplet-Loss function as well as the set of different goats images is complicated based on manual method.Next,Triplet-Loss function in dataset has its potentials compared with the siamese network method.Our joint loss function and transfer learning model residual neural network(ResNet1

关 键 词：深度学习 奶山羊个体身份识别 Triplet-Loss 联合损失函数 迁移学习 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]