摘要
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.
作者
李昊宸
刘天元
富雨超
李婉香
张猛
杨希
宋迪
王佳琪
王浟
黄梅珍
Haochen Li;Tianyuan Liu;Yuchao Fu;Wanxiang Li;Meng Zhang;Xi Yang;Di Song;Jiaqi Wang;You Wang;Meizhen Huang(Department of Instrument Science and Engineering,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Department of Electrical Engineering,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Southwest Institute of Technical Physics,Chengdu 610041,China)
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)。
