Performance evaluation of a deep learning based wet coal image classification. (1st September 2021)

• Record Type: Journal Article
• Authors: Liu, Yang; Zhang, Zelin; Liu, Xiang; Wang, Lei; Xia, Xuhui
• Is Part Of: Minerals engineering. Volume 171(2021)
• Publication Date: 2021-09-01
• Subjects: Moisture -- Wet ore -- Gangue -- Machine vision -- Deep learning; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2021.107126
• Languages: English
• ISSNs: 0892-6875

Abstract

Graphical abstract: Highlights: Present CNN model for various wet ore with different water content. Analyze the classification performance for different wet ore. Explore the operational process of CNN model for wet ore classification. Abstract: Moisture is one of the important influencing factors on machine vision-based mineral image classification, and it has different effects on various ore particles. At present, deep learning is an effective measure to improve classification accuracy, but the effects of moisture have not been systematically investigated. Therefore, this paper establishes deep learning-based RGB image classification models for the classification tasks of various coal particles with two density level (<1.8 g/cm 3 & >1.8 g/cm 3 ) in different water gradients, and analyzes their classification performance. Moreover, the model operational process and the change of classification weight and accuracy under different water gradients are investigated through Channel Visualization, Heatmap, Guided Backpropagation, Grad-CAM.