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    赵戈伟, 许升全, 谢娟英. DL-MAML:一种新的蝴蝶物种自动识别模型[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220860
    引用本文: 赵戈伟, 许升全, 谢娟英. DL-MAML:一种新的蝴蝶物种自动识别模型[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220860
    Zhao Gewei, Xu Shengquan, Xie Juanying. DL-MAML:An Innovative Model for Automatically Identifying Butterfly Species[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220860
    Citation: Zhao Gewei, Xu Shengquan, Xie Juanying. DL-MAML:An Innovative Model for Automatically Identifying Butterfly Species[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220860

    DL-MAML:一种新的蝴蝶物种自动识别模型

    DL-MAML:An Innovative Model for Automatically Identifying Butterfly Species

    • 摘要: 蝴蝶种类成千上万,每种蝴蝶都与一定植物密切相关,研究蝴蝶种类自动识别有重要意义. 野外环境下的蝴蝶物种识别研究受制于现有数据集蝴蝶种类较少,每类样本(图像)数量较少,使基于机器学习的蝴蝶种类识别面临泛化推广难挑战. 另外,野外环境下的蝴蝶翅膀遮挡使分类特征学习面临挑战. 因此,提出基于元学习的蝴蝶物种自动识别新模型DL-MAML(deep learning advanced model-agnostic meta-learning),实现野外环境下的任意蝴蝶种类识别. DL-MAML模型采用L2正则改进经典元学习算法MAML(model-agnostic meta-learning)的目标函数和模型参数更新方法,并对MAML增加了2层特征学习模块,避免模型陷入过拟合风险,解决现有野外环境下蝴蝶物种识别面临的泛化推广困难;其次,采用ResNet34深度学习模型提取蝴蝶分类特征,对图像进行表征预处理,作为DL-MAML模型元学习模块的输入,克服其特征提取不足的缺陷,以及野外环境下蝴蝶翅膀遮挡带来的分类特征学习困难. 大量消融实验以及与同类模型的实验比较表明,DL-MAML算法学习获得的初始模型参数对蝴蝶新类识别具有很好的效果,优于MAML和其他同类模型,对野外环境下的蝴蝶种类识别很有效,使利用现有野外环境下的蝴蝶数据集构造通用且完全的蝴蝶物种识别系统成为可能.

       

      Abstract: There are tens of thousands of butterfly species. Each butterfly species is closely related to a specific type of plants. It is significant to study butterfly species automatic identification. However, it is very challenging to study butterfly species recognition via the images taken in the field environment. One reason is that there are small number of butterfly species in the existing datasets compared to the reported species in the world. The other reason is that the number of samples (images) of each butterfly species is limited in the datasets. These situations make it challengeable to train a general system for butterfly species identification via machine learning algorithms. In addition, butterfly wings are always folded in the images taken in the field environment, which make it challengeable to learn butterfly classification features, which further make it difficult to study butterfly species recognition using machine learning techniques. Therefore, meta-learning is introduced to address the challenges, and the DL-MAML (Deep Learning Advanced Model-Agnostic Meta-learning) algorithm is proposed for identifying the butterfly species using available images taken in the field environment. It introduces a L2 regularization term to the objective function and the parameter updating of the meta-learning MAML while introducing ResNet34 deep learning model to extract butterfly features and plus two more convolution layers to the MAML model to avoid over-fitting. The extensive experiments including ablation experiments and comparison to available meta-learning models demonstrate that the initial model parameters obtained by DL-MAML algorithm are effective to identify new species butterflies. The DL-MAML is effective in identifying butterfly species using images taken in the field environments, which make it possible to construct the general and complete butterfly species identification system using available butterfly images from limited categories.

       

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