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投稿时间：2023-05-01 修订日期：2023-08-11 网络发布日期：2023-11-01
Abstract:Based on the Inception-v3 convolutional neural network model, the article features 453 images of 8 kinds of rocks, including gold ore, copper ore, iron ore, lead-zinc ore, granite, gneiss, marble and shale. Extraction and transfer learning, the transfer learning model of lithology classification is established, and the automatic identification and classification of lithology is realized. Four images of each type of rock were randomly selected as the test set for testing, and the remaining 421 images were used as the training set to participate in the training. After testing, the lithology classification results of all images were correct, and the rock images with a recognition accuracy rate of more than 80% accounted for more than 90% of the total number of images in the test set. The images whose recognition accuracy rate did not reach 80% were retrained and tested after processing, and the recognition accuracy rate exceeded 80%, indicating that the model has good lithology recognition ability and good robustness, and is an important tool which provides a new intelligent analysis method for lithology recognition and automatic classification.
keywords:lithology identification image classification transfer learning convolutional neural network intelligent identification
ZHAO XingDong,WANG HongYu,BAI Ye.2023.Mineralized and barren rock intelligent identification method based on convolutional neural network transfer learning model[J].Mineral Deposits42(5):1003~1010