Towards Efficient and Accurate Coal Classification: An Online Knowledge Distillation Approach With Dynamic Attention

ABSTRACT

Coal classification is essential for industrial production, quality control and intelligent sorting systems. Traditional laboratory‐based chemical analysis methods are often destructive and time‐consuming, while manual visual inspection remains subjective and inconsistent. To address these challenges, this paper proposes DP‐KDNet (dynamic perception and knowledge distillation network), an efficient coal image classification network that integrates a dynamic perception attention mechanism (DPAM) with an online knowledge distillation strategy.

The proposed DPAM enhances feature representation by dynamically fusing global statistical features, local variance information and multi‐scale contextual dependencies, enabling adaptive emphasis on discriminative coal surface characteristics such as lustre, texture and pore distribution. Meanwhile, the online distillation framework transfers knowledge from deeper network stages to shallow representations, improving classification robustness while maintaining computational efficiency.

Experiments on a self‐constructed coal image dataset containing anthracite, bituminous coal and lignite demonstrate that DP‐KDNet achieves 91% classification accuracy, outperforming the baseline ResNet50 by 7.8 percentage points (83.2%→91.0%). The improvements are consistent across all metrics (precision, recall, F1‐score and ROC‐AUC), confirming the robustness and practical significance of the proposed method. Further analysis confirms improved feature separability and competitive computational efficiency, suggesting the proposed framework is well‐suited for practical deployment in industrial environments.