计算机工程与应用2019，Vol.55Issue(1)：174-179,6.DOI:10.3778/j.issn.1002-8331.1709-0322
基于残差网络迁移学习的花卉识别系统
Flower Species Recognition System Based on Residual Network Transfer Learning
摘要
Abstract
Traditional flower species recognition algorithms are mainly based on designing hand-craft feature and training classifier, which generalization ability is limited and the recognition accuracy always reach a bottleneck. Therefore, this paper proposes a recognition method that is based on 152 residual layers deep convolutioal neural network. Specifically, model transfer learning is used to refine the network via large scale labeled flower database that are acquired from internet worm system. In the online system which has embedded the proposed recognition algorithm, user can send flower images to the cloud via internet. Then the recognition algorithm is performed through deep architecture that deployed on the server. Experimental results conducted on ImageNet and NetDragon datasets show that the fine tuned residual network has the advantages of high accuracy, real time feedback, and better robustness.关键词
深度学习/花卉识别/残差网络Key words
deep learning/flower species recognition/residual network分类
信息技术与安全科学引用本文复制引用
关胤..基于残差网络迁移学习的花卉识别系统[J].计算机工程与应用,2019,55(1):174-179,6.基金项目
福建省高校产学合作科技重大项目(No.2017H6015). （No.2017H6015）
