Deep learning is an effective way to improve the classification accuracy of coal images for the machine vision-based coal sorting. However, the related research on deep learning-based mineral image classification has not systematically considered the models for multi-coal and multi-class sorting. Additionally, the universal CNNs model for multi-coal image classification has not been proposed. Given the above problems, combined with deep learning and transfer learning and based on VGG Net, Inception Net, and Res Net, this study builds four CNNs models with different depth and structure for multi-coal and multi-class image classification. Finally, we take anthracite, gas coal, coking coal as the research objects and propose a universal CNNs model suitable for multi-coal and multi-class sorting. Moreover, with the Channel Visualization map, Heatmap, Gard-CAM map, and Guided Backpropagation map, the operational processes of CNNs model in coal image recognition and classification are revealed, and the features that affect the classification weights are analyzed.

深度学习是基于机器视觉的煤炭分选提高煤炭图像分类精度的有效途径。然而，基于深度学习的矿物图像分类的相关研究并没有系统地考虑多煤多类分选模型。此外，尚未提出用于多煤图像分类的通用 CNN 模型。针对上述问题，结合深度学习和迁移学习，基于VGG Net、Inception Net和Res Net，本研究构建了四种不同深度和结构的CNNs模型，用于多煤多类图像分类。最后，我们以无烟煤、瓦斯煤、炼焦煤为研究对象，提出了一种适用于多煤多类分选的通用CNNs模型。此外，借助频道可视化图、热图、