Coal and Gangue Infrared Images

This experiment was implemented to collect infrared images of the coal and gangue samples at the temperature of 323.15 K. Additionally, it showed that distinguishing between coal and gangue samples is feasible, although the area, thickness, and surface conditions were changed at a constant temperature during the process of capturing the infrared images. The coal and gangue were randomly collected from the same mine. The random samples had different weights, shapes, areas, thicknesses, and surface conations. The code is licensed under GNU Affero General Public License Version 3 (GNU AGPLv3); for more information, see https://www.gnu.org/licenses/agpl-3.0.en.html. The dataset (Coal and Gangue Infrared Images in BMP file format (Data.zip)) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. For more information, see https://creativecommons.org/licenses/by/4.0/. The code and data are connected to the article, entitled “Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)” TechRxiv, see, https://doi.org/10.36227/techrxiv.23266301.v3

本实验旨在收集在323.15开尔文温度下的煤和矸石样本的红外图像。此外，实验结果表明，在捕捉红外图像的过程中，尽管在恒定温度下样本的面积、厚度和表面状况发生了变化，但区分煤和矸石样本是可行的。煤和矸石样本均随机自同一矿井采集。随机样本具有不同的重量、形状、面积、厚度和表面特性。代码遵循GNU Affero通用公共许可证第3版（GNU AGPLv3）许可；欲获取更多信息，请参阅https://www.gnu.org/licenses/agpl-3.0.en.html。数据集（BMP文件格式的煤和矸石红外图像数据集（Data.zip））遵循Creative Commons署名4.0国际许可（CC BY 4.0）；欲获取更多信息，请参阅https://creativecommons.org/licenses/by/4.0/。代码和数据与题为《基于新技术和概念的计算机视觉深度学习算法：适用于煤制备厂二分类任务的PIDC-NN》的文章相关联，TechRxiv，详见https://doi.org/10.36227/techrxiv.23266301.v3。

搜集汇总

背景与挑战

背景概述

该数据集包含煤和矸石的红外图像，用于二元分类任务，如图像分类和识别。图像在恒定温度下采集，样本具有多样化的物理特性。数据集适用于机器学习算法的训练和测试，特别是计算机视觉领域的应用。