Coal/Gangue Recognition Using Convolutional Neural Networks and Thermal Images
| Autor: | Refat Mohammed Abdullah Eshaq, Jiaqi Zhao, Qiang Niu, Murad Saleh Alfarzaeai, Hu Eryi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
General Computer Science Computer science convolutional neural network 02 engineering and technology 01 natural sciences Convolutional neural network Thermal 0202 electrical engineering electronic engineering information engineering General Materials Science Coal coal gangue business.industry thermal images General Engineering Pattern recognition Coal gangue dataset augmentation Gangue object classification 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 010606 plant biology & botany |
| Zdroj: | IEEE Access, Vol 8, Pp 76780-76789 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.2990200 |
| Popis: | Recognition and separation of Coal/Gangue are important phases in the coal industries for many aspects. This paper addressed the topic of Coal/Gangue recognition and built a new model called (CGR-CNN) based on Convolutional Neural network (CNN) and using thermal images as standard images for Coal/Gangue recognition. The CGR-CNN model has been developed, augmentation principle has been applied in order to increase the dataset and the best experimental results have been achieved (99.36%) learning accuracy and (95.09%) validation accuracy, in the prediction phase (160) new images of coal and gangue (80 for both) have been tested to measure the efficiency of the work, the prediction result comes with (100%) for coal recognition accuracy and (97.5%) gangue recognition accuracy giving an overall prediction accuracy (98.75%). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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