A Dual-Energy X-Ray Differential Response Fusion Method for Three-Class Copper Ore Classification

Preconcentration before grinding is important for reducing unnecessary downstream processing and improving ore utilization. Dual-energy X-ray transmission imaging provides paired responses of ore particles under different energy levels, which can be used for particle-level classification. However, adjacent categories, such as waste rock and low-grade copper ore, may exhibit similar transmission appearances, and discriminative cues may be distributed across both local attenuation details and global transmission patterns. In this study, we propose a dual-energy X-ray image classification method, named the Difference-Guided Cross-Level Feature Fusion Network (DGCF-Net), for three-class copper ore classification. Waste rock and copper ore samples from the Dexing Copper Mine were used to construct a three-class dual-energy X-ray image dataset. DGCF-Net incorporates response-difference cues from paired low- and high-energy images and combines local and global feature representations for ore-particle classification. Experimental results on the constructed dataset show that the proposed method achieved an Overall Accuracy of 0.9570, a Macro-F1 of 0.9664, and an AUC of 0.9953, with 3.6424 M parameters. These results indicate that the proposed method provides effective classification performance on the current dataset, particularly for categories with relatively similar image responses, while its broader practical applicability requires further validation under more realistic operating conditions.