Research and Analysis of Image Enhancement Algorithm in the Classification of Rock Thin Section Images

2021 3rd International Conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP) Pub Date : 2021-07-23 DOI:10.1109/ICMSP53480.2021.9513355

Yang Huaizhou, Xu Danyang

{"title":"Research and Analysis of Image Enhancement Algorithm in the Classification of Rock Thin Section Images","authors":"Yang Huaizhou, Xu Danyang","doi":"10.1109/ICMSP53480.2021.9513355","DOIUrl":null,"url":null,"abstract":"The structure of rock flakes is complex and difficult to be classified accurately. The proposed method to solve the problem is to use an image enhancement algorithm to enhance the rock slice image. In the study, the neural network ResNet50, which has a significant effect on fine-grained classification, was used to construct the rock cast thin section image classifier, and three image enhancement algorithms, CutOut, MixUp, and CutMix, were used to enhance the rock thin section image. The rock slice images used in the data set are from Ordos, and are divided into five categories according to the size of the rock. The experimental result obtained was that the CutOut algorithm performs well on the data set, and the accuracy of the classifier was as high as 93.39%, which is 1.3% higher than the result of only using ResNet50 for classification. The experimental results show the effectiveness of the image enhancement algorithm in the classification of rock slice images.","PeriodicalId":153663,"journal":{"name":"2021 3rd International Conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSP53480.2021.9513355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

Abstract

The structure of rock flakes is complex and difficult to be classified accurately. The proposed method to solve the problem is to use an image enhancement algorithm to enhance the rock slice image. In the study, the neural network ResNet50, which has a significant effect on fine-grained classification, was used to construct the rock cast thin section image classifier, and three image enhancement algorithms, CutOut, MixUp, and CutMix, were used to enhance the rock thin section image. The rock slice images used in the data set are from Ordos, and are divided into five categories according to the size of the rock. The experimental result obtained was that the CutOut algorithm performs well on the data set, and the accuracy of the classifier was as high as 93.39%, which is 1.3% higher than the result of only using ResNet50 for classification. The experimental results show the effectiveness of the image enhancement algorithm in the classification of rock slice images.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

岩石薄片图像分类中图像增强算法的研究与分析

岩片结构复杂，难以准确分类。本文提出的解决方法是利用图像增强算法对岩石切片图像进行增强。本研究采用对细粒度分类效果显著的神经网络ResNet50构建岩石浇铸薄片图像分类器，并采用CutOut、MixUp和CutMix三种图像增强算法对岩石薄片图像进行增强。数据集中使用的岩石切片图像来自鄂尔多斯，并根据岩石的大小分为五类。实验结果表明，cut - out算法在数据集上表现良好，分类器准确率高达93.39%，比仅使用ResNet50进行分类的结果提高了1.3%。实验结果表明了图像增强算法在岩石切片图像分类中的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助