基于多层次的海洋生物分类
Multi-hierarchical Classification for Marine Organisms
赵东 1程远志2
作者信息
- 1. 青岛科技大学信息科学技术学院,青岛 266061
- 2. 青岛科技大学信息科学技术学院,青岛 266061;哈尔滨工业大学计算机科学与技术学院,哈尔滨 150001
- 折叠
摘要
本文提出了一种多层次海洋生物分类方法.海洋生物种类繁多,且同门类生物具有较强的类间相似性,而不同门类生物具有较大的差异.我们利用物种间的相似性,帮助网络学习生物先验知识,设计出了一种多层次分类方法.设计了C-MBConv模块,并结合多层次分类方法改进了EfficientNetV2 网络架构,改进后的网络架构称为CM-EfficientNetV2.我们的实验表明CM-EfficientNetV2 比原网络EfficientNetV2 有着更高的准确率,在南麂列岛潮间带海洋生物数据集上准确率提高了1.5%,在CIFAR-100上准确率提高了2%.
Abstract
This study proposes a multi-hierarchical classification method for marine organisms.Marine organisms are diverse,and organisms of the same phylum have strong inter-class similarity,while organisms of various phyla have large differences.Meanwhile,a multi-hierarchical classification method is designed by utilizing the similarity among species to help the network learn biological prior knowledge.Additionally,this study designs a C-MBConv module and improves the EfficientNetV2 network architecture by combining the multi-hierarchical classification method,and the improved network architecture is called CM-EfficientNetV2.The experiments show that CM-EfficientNetV2 has higher accuracy than the original network EfficientNetV2,with an accuracy improvement of 1.5%on the inter-tidal marine biology dataset of the Nanji Islands and 2%on CIFAR-100.
关键词
分类/多层次/卷积/海洋生物/图像识别/深度学习Key words
classification/multi-hierarchical/convolution/marine organism/image recognition/deep learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据