Reflectance-Consistent CycleGAN for Low-Sample Data Augmentation in Graphite Ore Grade Recognition

Accurate grade detection in graphite ore, which is a strategic and critical mineral resource, plays an important role in improving beneficiation efficiency and overall resource utilization. However, the scarcity of high-grade samples limits the performance of deep learning models in grade identification tasks. This limitation makes it difficult for models to learn stable and representative features. This paper proposes an enhanced CycleGAN-based image augmentation framework designed for graphite ore imagery. The method works within an unpaired image translation architecture. It introduces a distributed reflectance consistency loss. This loss encodes the graphite ore’s typical low reflectance and high optical contrast as explicit statistical constraints. The design enforces consistency in both the intensity distribution and the textural structure of the generated images. The model further integrates a convolutional block attention module into the generator. This module helps refine feature representation under a physics-inspired heuristic. The study constructs augmented training sets using the proposed method. It then evaluates these datasets with a downstream grade classification model. Experimental results show clear improvements. The method reduces Fréchet Inception Distance by 21.9% and Kernel Inception Distance by 39.4%. It also improves peak signal-to-noise ratio by 3.3% and structural similarity index measure by 2.6% compared with the baseline CycleGAN. The classification accuracy in the grade identification task increases by about 2.3 percentage points. These results show that the proposed method improves both the perceptual quality and the statistical consistency of synthetic graphite ore images. It also helps reduce the performance drop caused by limited training data in few-shot learning conditions.