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Automated segmentation of gravel particles from depth images of gravel-soil mixtures

机译：砾石土混合物深度图像砾石粒子的自动分割

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摘要

We propose an image-based technique to measure the volume, weight and the size distribution of gravel particles in a gravel-soil mixture. The proposed method uses 3D scanning and a surface reconstruction algorithm to generate a high-resolution depth image, which is then used to accurately estimate the volume and weight of each gravel particle. The proposed method is evaluated on several gravel soil samples collected from 25 farming locations. The experimental results show that the proposed technique produces an accurate estimate of gravel volumes and gravel weights. It achieves a relative root mean square error of 4% for large gravel particles and an overall correlation of 0.99 with the ground truth, for the task of gravel volume estimation. For the estimation of gravel weight distribution, the proposed method can reach a low root mean square error of 0.54%. The rapid measurement of the full spectrum of coarse fragments in soil, using this method, is an advantage compared to the manual methods.
机译：我们提出了一种基于图像的技术来测量砾石土混合物中砾石颗粒的体积，重量和尺寸分布。该方法使用3D扫描和表面重建算法来产生高分辨率深度图像，然后用于精确地估计每个砾石颗粒的体积和重量。所提出的方法是在从25个农业位置收集的几个砾石土样本上进行评估。实验结果表明，该技术生产砾石体积和砾石重量的准确估计。对于大型砾石颗粒，实现了4％的相对根均方误差和0.99与地面真相的总相关性，用于砾石体积估计的任务。为了估计砾石重量分布，所提出的方法可以达到0.54％的低根均方误差。使用该方法的土壤中全光谱的快速测量粗裂片，是与手动方法相比的优势。

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