基于介观岩石图像深度学习的便携式岩性智能识别
Portable Intelligent Lithology Identification via Deep Learning of Mesoscopic Rock Images
马文 1李珊 1赵新鹏 2郑艳伟 2于东晓 2林鹏 1王旭龙 2许振浩1
作者信息
- 1. 隧道工程灾变防控与智能建养全国重点实验室,山东济南 250061;山东大学齐鲁交通学院,山东济南 250061
- 2. 山东大学计算机科学与技术学院,山东青岛 266237
- 折叠
摘要
针对传统岩性识别依赖人工经验、主观性强、耗时耗力的问题,提出了一种基于介观岩石图像深度学习的现场岩性智能识别方法.采用卷积神经网络并结合迁移学习方法,构建了岩性识别模型.采用身份采样器以多样化的方式选择样本构建训练批次,保证了模型不会偏向数据量较多的样本,提高了推理的准确性.将经过训练的Pytorch模型转换成ONNX模型部署到便携式设备上,设计和开发了 一款专用于岩性识别的软件.在此软件中,电子放大镜可用于拍摄介观岩石图像,实现了对岩石岩性的准确判识.研究结果表明,该方法的识别准确率最高可达99％,可满足地质调查人员在野外作业中快速获取岩石岩性的需求.
Abstract
A field intelligent lithology identification method was proposed based on the deep learning of mesoscopic rock image to address the problems of traditional method relying on manual experience,strong subjectivity,and time-consuming and labor-intensive.Convolutional neural networks combined with transfer learning methods was used to constructed the lithology identification model.The identity sampler was used to select samples in a diverse way to construct training batches,ensuring that the model does not bias towards samples with large amounts of data and improving the accuracy of inference.Then,the trained Pytorch model was transformed into an ONNX model and deployed on the portable device,and a software specifically designed for lithology identification was designed and developed.In this software,electronic magnifying glasses can be used to capture mesoscopic rock images,achieving accurate lithology identification.The results show that the identification accuracy of this method can reach up to 99％,which can meet the need of geological investigators to quickly obtain rock lithology in field operations.
关键词
岩性识别/深度学习/人工智能/图像识别/模型部署/岩石图像Key words
lithology identification/deep learning/artificial intelligence/image identification/model deployment/rock images引用本文复制引用
基金项目
国家自然科学基金项目(52021005)
国家自然科学基金项目(52279103)
国家自然科学基金项目(52379103)
出版年
2024
NETL
NSTL
万方数据