利用迁移学习和焦点损失卷积神经网络的石墨分类

Graphite Classification Using Transfer Learning and Focal Loss Convolutional Neural Network

为了使得优质石墨资源得到优质优用,提出利用迁移学习和焦点损失卷积神经网络的石墨分类识别算法.在自建的初始数据集基础上,通过对数据集的离线扩充与在线增强,有效扩大数据集并减低深层CNN过拟合的风险.以VGG16、ResNet34和MobileNet V2为基础模型,重新设计新的输出模块载入全连接层,提高了模型的泛化能力与鲁棒性;结合焦点损失函数,修改模型超参数并在石墨数据集上训练.实验仿真发现,本文所提方法的准确率均在95%以上,识别准确率提高,收敛速度加快,模型更加稳定,证明了所提算法的可行性与有效性.

For better use of high-quality graphite resources, this study proposes a graphite classification and recognition algorithm based on transfer learning and focal loss convolutional neural network(CNN). The offline expansion and online enhancement of the self-built initial data set can effectively expand the data set and reduce the overfitting risk of deep CNN. With VGG16, ResNet34, and MobileNet V2 as basic models, a new output module is redesigned and loaded into the full connection layer, which improves the generalization ability and robustness of the model. Combined with the focal loss function, the hyperparameters of the model are modified and trained on the graphite data set. The simulation results show that the proposed method has the accuracy improved to above 95% with faster convergence and a more stable model, which proves the feasibility and effectiveness of the proposed algorithm.