矿料级配质量是影响路面性能最重要的因素，而光学三维手段能够提供矿料级配质量实时、自动的检测。然而堆叠矿料在颗粒偏析、颗粒重叠粘连等因素的影响下，由于对堆叠矿料粒度分布与其表层粒度分布的关联和影响机制不明确，使得当前颗粒粒度分布表征方法存在精度较低且不稳定等问题，亟需进一步研究。本项目拟首先针对矿料颗粒三维特征，提出颗粒的三维表征模型，以实现颗粒特性的精确定量表征；然后针对堆叠矿料表面层粘连颗粒，提出颗粒图像分割算法，并建立表面层颗粒概率模型；最后通过提取表面层颗粒多尺度数据，并结合表面层颗粒概率模型，建立堆叠矿料粒度分布预测模型。通过本项目的研究，实现堆叠颗粒高精度、高鲁棒性的粒度分布表征，其研究成果可以为矿料级配自动分析领域提供较好的理论基础和技术支撑。

The quality of aggregate gradation has a very significant impact on the pavement performance, and the 3D machine vision system can provide real-time automatic detection for the aggregate gradation. However, because of the correlation and impact mechanism of the stacked ore material particle size distribution and its surface particle size distribution was not studied clearly, the current particle size characterization method has lower accuracy and is not stable when the particles distribution measurement system is influenced by the particles segregation and particles overlap. To solve the problem, firstly, in order to take a quantitative description for the 3D particles, a new 3D particles size measurement algorithm will be proposed. Secondly, for the overlapped surface particles of the stacked ore material, a kind of overlapped particles segmentation method will be proposed and the surface particles probability model will be established. Lastly, by combining the surface particles probability model and the multi-scale dates of the surface particles, a kind of particle size distribution prediction model based on machine learning technique will be developed. In this project, for the stacked ore material, a particle size distribution method with high precision and robustness will be established, and also the findings will provide a good theoretical base and technological support for the aggregates gradation automatic analysis.