用于铁矿石表征的反射光显微铁矿石图像数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-04-03 DOI:10.1016/j.dib.2025.111540

Shama Firdaus , Shamama Anwar , Subrajeet Mohapatra , Prabodha Ranjan Sahoo

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引用次数: 0

摘要

数据集包含两个文件夹“IronOreRLM”和“Sample Images”。样本图像文件夹包含了研究中每个年级的少量图像，总共有12张图像。此文件夹类似于完整数据集的摘要，创建此文件夹是为了预览目的。IronOreRLM文件夹是主要数据集，包含从印度各个矿山收集的铁矿石的563张反射光显微镜（RLM）图像。这些RLM图像是关于矿石的宝贵信息来源，提供了对组成元素、矿石质量、结构等的见解。可以对该数据集进行各种分析，以从图像中提取有意义的信息。获取该数据集的主要目标是通过利用计算机视觉的能力来自动化矿物加工中的化学广泛任务。虽然本文引用了与数据集相关的研究工作，但并不限制数据集的范围。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Reflected Light Microscopic Iron ore image dataset for iron ore characterization

The dataset contains two folders “IronOreRLM” and “Sample Images”. The folder Sample Images contains few images from each of the grades included in the study and has total of 12 images. This folder is like an abstract of the full dataset and has been created for preview purpose. The IronOreRLM folder is main dataset containing a total of 563 reflected light microscopic (RLM) images of iron ores collected from various mines across India. These RLM images are a valuable source of information about the ores, providing insights into constituent elements, ore quality, structure, and more. Various analyses can be conducted on this dataset to extract meaningful information from the images. The primary goal of acquiring this dataset is to automate the chemical-extensive tasks in mineral processing by leveraging the capabilities of computer vision. While the research work associated with the dataset has been cited in this article, it does not limit the scope of the dataset.

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.