Aggregate is an important component of asphalt mixtures, and its shape has a significant influence on the road quality. In this study, a single industrial camera was used to collect images of aggregate particles during falling; then, their morphologies obtained in multiple views were analyzed. Using the equivalent geometric model, four shape characterization parameters—area variety factor, minor diameter variety factor, maximum elongation factor, and Strip-Block area variety factor—were proposed to compose the multi-view shape feature. On this basis, a general regression neural network was adopted to realize the classification of aggregate particles. The results show that the aggregate classification is slightly different when using different equivalent geometric models, while the aggregate shape can be effectively classified. The accuracy of aggregate classification can be improved by fusing parameters from different equivalent models using principal component analysis; another way is through increasing the frame rate of image collection that may increase the number of views. In general, the findings indicate that the proposed detection method can be applied to actual road engineering, which is of great significance to guarantee pavement quality.

集料是沥青混合料的重要组成部分，其形状对路面质量有显着影响。在这项研究中，单个工业相机用于收集坠落过程中聚集颗粒的图像；然后，分析了它们在多个视图中获得的形态。利用等效几何模型，提出四个形状表征参数——面积变异系数、小径变异系数、最大伸长系数和条块面积变异系数——构成多视图形状特征。在此基础上，采用通用回归神经网络实现骨料颗粒的分类。结果表明，当使用不同的等效几何模型时，骨料分类略有不同，而骨料形状可以有效分类。通过主成分分析融合不同等效模型的参数，可以提高骨料分类的准确性；另一种方法是通过提高图像收集的帧率，这可能会增加视图的数量。总的来说，研究结果表明所提出的检测方法可以应用于实际的道路工程，对保证路面质量具有重要意义。