Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Medinipur, 721302, West Bengal, India.
Sci Rep. 2023 Feb 4;13(1):2046. doi: 10.1038/s41598-023-28586-0.
The stability of mine overburden dumps is crucial for the efficient operation of mining industries. The size distribution of particles affects the shear strength of dump slopes. Identification of dump particles from images is challenging as they vary in size, shape, color, granularity, and texture. In this paper, a unique way of identifying the particles from dump images using Artificial Intelligence is presented that can be used to determine the particle size distribution of dump. Mask R CNN with ResNet50 plus an FPN as a backbone network which is the current state of the art for instance segmentation has been implemented to segment the particles from dump images at detailed pixel level and to obtain their boundary. Experimental results showed promising results to delineate the particles and obtain masks over them. Our model has achieved a training accuracy of 97.2% for the dataset containing 31,505 particles. The model predicted the areas of dump particles with a mean percentage error of 0.39% and a standard deviation of 0.25 when compared to the ground truth values. The calculation of coordinates of the detected boundaries using the model significantly reduces the time and effort that are generally put in rock mechanics laboratories.
矿山覆岩堆稳定性对采矿业的高效运行至关重要。颗粒的粒度分布会影响堆坡的抗剪强度。由于堆料中的颗粒大小、形状、颜色、粒度和纹理各不相同，因此从图像中识别这些颗粒具有挑战性。本文提出了一种使用人工智能从堆料图像中识别颗粒的独特方法，可用于确定堆料的颗粒粒度分布。使用 Mask R-CNN 结合 ResNet50 加 FPN 作为骨干网络（实例分割的最新技术），以详细的像素级对堆料图像中的颗粒进行分割，并获取它们的边界。实验结果表明，该方法在描绘颗粒并获得其覆盖的掩模方面取得了有前景的结果。我们的模型在包含 31,505 个颗粒的数据集上实现了 97.2%的训练精度。与地面真实值相比，该模型预测堆料颗粒区域的平均百分比误差为 0.39%，标准偏差为 0.25%。使用该模型计算检测边界的坐标，可以显著减少岩石力学实验室通常投入的时间和精力。
