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<p>Accurate and efficient coal classification is essential for optimizing energy production, improving resource utilization, and ensuring environmental compliance in industrial applications. Traditional methods that rely on visual inspection and laboratory analysis are often slow, subjective, and difficult to scale. In response to these challenges, this research proposes a custom task adaptive lightweight convolutional neural network that integrates Separable Convolutional Blocks, Inverted Residual Blocks, and Squeeze and Excitation mechanisms to achieve high classification performance with low computational overhead. The model is trained and evaluated on the large scale DsCGF dataset, which comprises over 270,000 images of coal, gangue, foreign objects, and unknown materials collected under real world production and non-production conditions from multiple mining regions. The experimental results show that the proposed model achieves classification accuracies of 93.73% on Anhui Guobei production dataset, 99.97% on the Anhui Guobei non-production dataset, 99.26% on the Inner Mongolia Erlintu production dataset, and 90.50% on the challenging Shanxi Wangjialing production dataset, while using only 1.72&#xa0;million parameters and achieving an average inference time of 0.023&#xa0;s per image. Compared with well-established transfer learning models such as MobileNetV2, DenseNet201, and Xception, the proposed architecture consistently demonstrates superior or comparable performance across accuracy, precision, recall, and F1 score metrics with significantly reduced computational complexity. To enhance interpretability and provide visual insights into model predictions, explainable artificial intelligence techniques including Grad-CAM, Grad-CAM++, and Score-CAM are employed to visualize class discriminative regions. The proposed framework provides a practical, interpretable, and resource efficient solution for coal classification and contributes meaningfully to the advancement of intelligent mining systems.</p>

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A lightweight explainable deep learning framework for coal classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月13日

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  • Avijit Paul,
  • Farjahan Akter Boby,
  • Yamina Islam,
  • Fariha Haque,
  • Yeasin Arafat,
  • Mohammad Asif Hasan,
  • Mominul Ahsan,
  • Hanaa A. Abdallah

摘要

Accurate and efficient coal classification is essential for optimizing energy production, improving resource utilization, and ensuring environmental compliance in industrial applications. Traditional methods that rely on visual inspection and laboratory analysis are often slow, subjective, and difficult to scale. In response to these challenges, this research proposes a custom task adaptive lightweight convolutional neural network that integrates Separable Convolutional Blocks, Inverted Residual Blocks, and Squeeze and Excitation mechanisms to achieve high classification performance with low computational overhead. The model is trained and evaluated on the large scale DsCGF dataset, which comprises over 270,000 images of coal, gangue, foreign objects, and unknown materials collected under real world production and non-production conditions from multiple mining regions. The experimental results show that the proposed model achieves classification accuracies of 93.73% on Anhui Guobei production dataset, 99.97% on the Anhui Guobei non-production dataset, 99.26% on the Inner Mongolia Erlintu production dataset, and 90.50% on the challenging Shanxi Wangjialing production dataset, while using only 1.72 million parameters and achieving an average inference time of 0.023 s per image. Compared with well-established transfer learning models such as MobileNetV2, DenseNet201, and Xception, the proposed architecture consistently demonstrates superior or comparable performance across accuracy, precision, recall, and F1 score metrics with significantly reduced computational complexity. To enhance interpretability and provide visual insights into model predictions, explainable artificial intelligence techniques including Grad-CAM, Grad-CAM++, and Score-CAM are employed to visualize class discriminative regions. The proposed framework provides a practical, interpretable, and resource efficient solution for coal classification and contributes meaningfully to the advancement of intelligent mining systems.