Research on graphite ore grade classification based on the integration of multi-level features from ResNet and transformer

Traditional graphite ore production typically relies on carbon-sulfur analyzers to determine the carbon grade of graphite ore. This method, however, is cumbersome and lacks timeliness. To address these issues, we propose TRA-FFNet, a graphite carbon grade image recognition and classification model that integrates multi-level features from ResNet and Transformer. Initially, ResNet-50 is employed as the backbone feature extraction network, with model parameters initialized through transfer learning to accelerate convergence. Subsequently, a Transformer module based on spatial feature compression fusion is designed to capture the weight relation-ships among different channels of the graphite ore feature map. Finally, a multi-level feature fusion module is incorporated at the network's terminal position to enhance the joint learning of both global and local features in graphite ore images. Experimental results show that the pro-posed model achieves an accuracy of 93.473% and an F1 score of 94.023% on our self-constructed dataset containing 19 classes of graphite ore, outperforming classic models such as MobileNetv2, EfficientNet-b3, ConvNext, RepVgg, and Swin Transformer. The proposed model achieves high-precision recognition of graphite ore grade in an end-to-end manner, offering a valuable detection solution for smart mining operations.