改进胶囊网络的小样本光伏热斑识别方法

Improved capsule network method for small sample photovoltaic hot spot recognition

为了提高小样本条件下光伏电池片热斑分类模型的特征提取能力,该文提出一种基于迁移学习的注意力胶囊网络。该网络不同于传统胶囊网络单一卷积层的特征提取,它是利用迁移学习的方法通过VGG-19网络和注意力机制进行特征提取,得到关键数据特征。然后构造向量神经元输入胶囊网络,利用动态路由算法,得到用以分类的数字胶囊层,实现光伏电池片的分类。实验结果表明:当样本数量较小时,该网络对电池片红外图像的识别准确率分别高于传统卷积神经网络和胶囊网络13.15%、7.91%,泛化能力更强,运算速度更快。

In order to improve the feature extraction ability of the photovoltaic cell hot spot classification model under the condition of small samples,this paper proposes an attention capsule network based on transfer learning This network is different from the feature extraction of a single convolutional layer of the traditional capsule network.It uses the method of migration learning to extract the features through the VGG-19 network and the attention mechanism to obtain key data features.Then construct the vector neuron input capsule network,and use the dynamic routing algorithm to obtain the digital capsule layer for classification to realize the classification of photovoltaic cells.The experimental results show that when the number of samples is small,the recognition accuracy of the infrared image of the battery is higher than that of the traditional convolutional neural network and the capsule network by 13.15%and 7.91%,with stronger generalization ability and faster calculation speed.