基于深度学习的砂岩组分显微图像识别  

Microscopic Image Recognition of Sandstone Components Based on Deep Learning

作　　者：任书杰 胡勇 何文祥 高小洋 万涛 REN Shu-jie;HU Yong;HE Wen-xiang;GAO Xiao-yang;WAN Tao(School of Resources and Environment,Yangtze University,Wuhan 430100,China;Institute of Geophysical Exploration,Zhongyuan Oilfield,SINOPEC,Puyang 457000,China)

机构地区：[1]长江大学资源与环境学院,武汉430100 [2]中国石化中原油田物探研究院,濮阳457000

出　　处：《科学技术与工程》2024年第9期3727-3736,共10页Science Technology and Engineering

摘　　要：识别砂岩中的石英、长石和岩屑对判断沉积环境具有重要意义,但传统的人工识别方法存在主观性强、对经验依赖程度高等问题。利用深度学习、卷积神经网络等技术构建了一种基于Faster R-CNN目标检测算法的砂岩显微组分图像识别方法,实现了正交偏光下对薄片图像中石英、长石、岩屑3种组分的智能识别,3种组分平均识别准确率为89.28%。为了验证模型的可靠性,实验对比了不同算法和特征提取网络,结果表明:Faster R-CNN目标检测算法的识别效果优于YOLOv3、YOLOv4、YOLOv5s,ResNet50特征提取网络的表现效果优于VGG16。采用ResNet50特征提取网络的Faster R-CNN目标检测模型优势显著,它可以更好满足岩石薄片的识别要求,为传统的人工方法提供智能化技术方案。Identifying quartz,feldspar and cuttings in sandstone is of great significance for judging the dispositive environment,but traditional manual identification methods have problems of strong subjectivity and high dependence on experience.An image recognition method of sandstone micro-components based on Faster R-CNN targeted detection algorithm was constructed by using deep learning,convolutional neural network and other technologies.Intelligent recognition of three components,the average recognition accuracy of the three components is 89.28%.In order to verify the reliability of the model,the experiments compared different algorithms and feature extraction networks.The results show that the recognition effect of the Faster R-CNN targets detection algorithm is better than that of YOLOv3,YOLOv4,and YOLOv5s,and the performance of the ResNet50 features extraction network is better than VGG16.The Faster R-CNN targets detection model using the ResNet50 features extraction network has significant advantages,it can better meet the identification requirements of rock slices,and provide intelligent technical solutions to traditional manual methods.

关 键 词：深度学习 卷积神经网络 Faster R-CNN 图像识别 ResNet50 岩石薄片 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] P588[自动化与计算机技术—计算机科学与技术]