摘要
蝴蝶分类是保护蝴蝶物种多样性、观测大气变化的首要工作。为了提高蝴蝶种类识别的准确率,改善复杂网络算法运行时间长的缺陷,提出了一种基于迁移学习与改进型AlexNet的蝴蝶分类算法。该算法将AlexNet作为预训练模型,使其成为新模型的特征提取器,并在AlexNet算法的基础上,通过调整卷积核数量、替换归一化LRN(local response normalization)层、减少全连接层个数、增加均值下采样层等,进行改进与优化。实验结果表明,改进算法对蝴蝶种类识别的准确率高于原AlexNet算法,并具有更优的识别效率,提升了整体模型的性能。
The butterfly classification algorithm aims to protect butterfly species diversity and observe atmospheric changes.In this study,we propose a butterfly classification algorithm based on transfer learning and improve AlexNet for more accurate identification of butterfly species and to resolve the issue of the long running time of the complex network algorithm.The proposed algorithm considers AlexNet as the pre-training model and uses it as a feature extractor for the new model.For this,we adjust the number of convolution kernels,replace the local response normalization layer,reduce the number of full connected layers,and increase the mean-pooling layer.Our experimental results show that the improved algorithm has higher accuracy and better recognition efficiency than the original AlexNet algorithm for butterfly species recognition.
作者
杨大为
蔡宇
YANG Dawei;CAI Yu(School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110158,China)
出处
《信息与控制》
CSCD
北大核心
2023年第4期514-524,共11页
Information and Control
基金
辽宁省教育厅科学研究经费项目(LG201915)
沈阳理工大学科研创新团队建设计划(SYLUTD202105)
作者简介
杨大为(1976-),男,博士,副教授。研究领域为机器学习,数字图像处理;通信作者:蔡宇(1999-),男,硕士生。研究领域为图像处理,计算机视觉,2861897852@qq.com。
