Li Chengwu, Ai Dihao
Faculty of Resources and Safety Engineering, China University of Mining and Technology, Beijing, China.
PLoS One. 2017 Oct 3;12(10):e0185750. doi: 10.1371/journal.pone.0185750. eCollection 2017.
In the coal mining process, the destabilization of loaded coal mass is a prerequisite for coal and rock dynamic disaster, and surface cracks of the coal and rock mass are important indicators, reflecting the current state of the coal body. The detection of surface cracks in the coal body plays an important role in coal mine safety monitoring. In this paper, a method for detecting the surface cracks of loaded coal by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM). A large number of cracked images are obtained by establishing a vibration-induced failure test system and industrial camera. Histogram equalization and a hysteresis threshold algorithm were used to reduce the noise and emphasize the crack; then, 600 images and regions, including cracks and non-cracks, were manually labelled. In the crack feature extraction stage, eight features of the cracks are extracted to distinguish cracks from other objects. Finally, a crack identification model with an accuracy over 95% was trained by inputting the labelled sample images into the SVM classifier. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm and can effectively identify cracks on the surface of the coal and rock mass automatically.
在煤矿开采过程中，加载煤体的失稳是煤岩动力灾害的前提条件，而煤岩体表面裂缝是反映煤体当前状态的重要指标。煤体表面裂缝检测在煤矿安全监测中具有重要作用。本文基于煤体表面裂缝特征和支持向量机（SVM），提出了一种通过振动破坏过程检测加载煤体表面裂缝的方法。通过建立振动诱发破坏试验系统和工业相机获取大量裂纹图像。采用直方图均衡化和滞后阈值算法降低噪声并突出裂缝；然后，人工标记了包括裂缝和非裂缝在内的600个图像和区域。在裂缝特征提取阶段，提取裂缝的八个特征以区分裂缝与其他物体。最后，将标记的样本图像输入SVM分类器，训练出准确率超过95%的裂缝识别模型。实验结果表明，所提算法比传统算法具有更高的准确率，能够有效自动识别煤岩体表面的裂缝。
