This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer ...This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer learning.Four pretrained CNN models were compared.The LIBS profiles were augmented into 2D matrices.Three transfer learning methods were tried.All the models got a high classification accuracy of>92%,with the highest at 96.2%for VGG16.These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting.The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.展开更多
基金supported by the Open Foundation of Key Laboratory of Laser Device Technology,China North Industries Group Corporation Limited(No.KLLDT202109)the National Natural Science Foundation of China(No.62175150)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(No.SL2021ZD103)。
文摘This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer learning.Four pretrained CNN models were compared.The LIBS profiles were augmented into 2D matrices.Three transfer learning methods were tried.All the models got a high classification accuracy of>92%,with the highest at 96.2%for VGG16.These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting.The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.
