基于改进VGG16网络模型的花卉分类

为进一步提高花卉分类的准确率, 在对现有的VGG16网络模型进行研究的基础上, 提出一种基于视觉注意力机制的网络模型. 将SE视觉注意力模块嵌入到VGG16网络模型中, 实现了对花卉显著性区域特征的提取; 为有效防止梯度爆炸及梯度消失, 加快网络的训练和收敛的速度, 在各卷积层后加入BN层; 采用多损失函数融合的方式对新模型进行训练. 新模型能有效提取花卉的花蕊、花瓣等显著性区域, 放大了花卉的类间距离, 缩小了类内距离, 加快了网络的收敛, 进一步提高了花卉分类的准确率. 实验结果表明, 新模型在Oxford-102数据集上的分类准确率比未引入注意力前有较大提高, 与参考文献相比, 分类准确率也有较大的提高.

Flower Classification Based on Improved VGG16 Network Model

To further improve the accuracy of flower classification, this study proposes a network model based on visual attention mechanism after the research on the VGG16 network model. Squeeze-and-excitation (SE) attention is embedded in the VGG16 network model to extract salient region features of flowers. BN layer is added following the convolutional layer to effectively prevent gradient explosion and gradient disappearance and to speed up the training and convergence of the network. Multi-loss function fusion is utilized to train the new model. The new model can effectively extract salient regions such as stamens and petals. It can enlarge the distance between and within classes and accelerate the convergence, further improving the classification accuracy. Experimental results show that the new model advances in the classification accuracy of the Oxford-102 dataset after the introduction of the attention mechanism and outstrips the current reference.