现代电子技术2024，Vol.47Issue(7)：37-44,8.DOI:10.16652/j.issn.1004-373x.2024.07.006
基于Swin Transformer的岩石岩性智能识别研究
Research on rock lithology intelligent identification based on Swin Transformer
摘要
Abstract
Due to the limitations of their receptive fields and the method of local processing,conventional convolutional neu-ral networks(CNNs)struggle with high recognition accuracy when identifying rock images with varied textures.In order to accu-rately identify rock lithology in complex scenarios and thereby improve the efficiency of geological surveys,a rock identification method based on improved Swin Transformer is proposed.A spatial local perception module is incorporated into this method.The Transformer's self-attention mechanism is combined to enhance the extraction of local correlations.To enhance generalization,a Dropout layer is added to the model,which reduces the dependence on individual neurons.To further enhance the generalization capability of the model,the AugMix algorithm is employed for rock image data augmentation.In combination with transfer learning technology,the network is pre-trained to optimize its parameters.Experimental results show that the accuracy of the proposed method reaches 96.4%,which outperforms that of networks like ResNet50,GoogLeNet and VGG16.关键词
岩石识别/深度学习/Transformer/自注意力机制/计算机视觉/识别精度Key words
rock identification/deep learning/Transformer/self-attention mechanism/computer vision/identification accuracy分类
信息技术与安全科学引用本文复制引用
韩鑫豪,何月顺,陈杰,熊凌龙,钟海龙,杜萍,田鸣..基于Swin Transformer的岩石岩性智能识别研究[J].现代电子技术,2024,47(7):37-44,8.基金项目
江西省放射性地学大数据技术工程实验室开放基金课题(JELRGBDT202203) （JELRGBDT202203）
