The Automatic extraction of coal particles characteristics has great importance in smart mine construction for security planning and disaster prevention. Traditional approaches, including visual interpretation, which requires manually outlining textures to describe particles, provide unclear texture characteristics due to the complexity of low-contrast grey particle imagery. Thus, a novel feature learning approach with a simplified VGGNet-like network is investigated to learn the characteristic details of complex spatial particle sample image sets. The sample data information includes 2000 particle images of four scenes acquired from a coal preparation plant. First, the particle overlapping regions are located by the detected feature points. Then particle swarms are separated with positioning-labels by their discriminative characteristics. Afterwards, feature classification by fully connected layers and image segmentation with up-sampling module introduced are realized based on improved VGGNet. Furthermore, particle sizes are evaluated over the hybrid particle distribution by characteristic learning. The experimental results demonstrate that, under the proper conditions, improved discrimination performance can be achieved by the proposed approach compared with that of other state-of-the-art approaches. The extraction performance can indeed be an effective reference to determine the particle size distribution (i.e., the granulometric analysis) of the sampled particulate.

煤粒特征的自动提取在智慧矿山建设中对于安全规划和灾害预防具有重要意义。传统的方法，包括视觉解释，需要手动勾勒纹理来描述粒子，由于低对比度灰色粒子图像的复杂性，提供不清楚的纹理特征。因此，研究了一种具有简化的类 VGGNet 网络的新特征学习方法，以学习复杂空间粒子样本图像集的特征细节。样本数据信息包括从选煤厂采集的四个场景的2000幅粒子图像。首先，粒子重叠区域由检测到的特征点定位。然后粒子群通过它们的判别特征与定位标签分开。之后，基于改进的VGGNet实现了全连接层的特征分类和引入上采样模块的图像分割。此外，通过特征学习在混合粒子分布上评估粒子大小。实验结果表明，在适当的条件下，与其他最先进的方法相比，所提出的方法可以提高区分性能。提取性能确实可以作为确定采样颗粒的粒度分布（即粒度分析）的有效参考。通过特征学习在混合粒子分布上评估粒子大小。实验结果表明，在适当的条件下，与其他最先进的方法相比，所提出的方法可以提高区分性能。提取性能确实可以作为确定采样颗粒的粒度分布（即粒度分析）的有效参考。通过特征学习在混合粒子分布上评估粒子大小。实验结果表明，在适当的条件下，与其他最先进的方法相比，所提出的方法可以提高区分性能。提取性能确实可以作为确定采样颗粒的粒度分布（即粒度分析）的有效参考。