Performance evaluation of a deep learning based wet coal image classification

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 Backpmpagation, Grad-CAM.

Automated summary

This paper investigates the application of deep learning in mineral image classification, establishing classification models for different coal particles and analyzing their performance under varying water gradients using techniques such as Channel Visualization, Heatmap, Guided Backpmpagation, and Grad-CAM.