Abstract 

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 

Instructions: 

These data are infrared images of coal and gangue in BMP format. You can use them to train and test your classifier such as SVM, CNN, etc.

These data are licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

 

Comments

These data are infrared images of coal and gangue in BMP format. You can use them to train and test your classifier such as SVM, CNN, etc.

These data are licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Submitted by REFAT ESHAQ on Mon, 06/05/2023 - 00:52

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