Particle size and morphological/shape properties ensure the reliable and sustainable use of all aggregate skeleton materials placed as constructed layers in transportation applications. The composition and packing of these aggregate assemblies rely heavily on particle size and morphological properties, which affect layer strength, modulus, and deformation response under vehicular loading and therefore facilitate the quality assurance/quality control (QA/QC) process. Aggregate imaging systems developed to date for size and shape characterization, however, have primarily focused on measurement of separated or slightly contacting aggregate particles. Development of efficient computer vision algorithms is urgently needed for image-based evaluations of densely stacked (or stockpile) aggregates, which requires image segmentation of a stockpile for the size and morphological properties of individual particles. This paper presents an innovative approach for automated segmentation and morphological analyses of stockpile aggregate images based on deep learning techniques. A task-specific stockpile aggregate image dataset is established from images collected from various quarries in Illinois. Individual particles from the stockpile images are manually labeled on each image associated with particle locations and regions. A state-of-the-art object detection and segmentation framework called Mask R-CNN is then used to train the image segmentation kernel, which enables user-independent segmentation of stockpile aggregate images. The segmentation results show good agreement with ground-truth labeling and improve the efficiency of size and morphological analyses conducted on densely stacked and overlapping particle images. Based on the presented approach, stockpile aggregate image analysis promises to become an efficient and innovative application for field-scale and in-place evaluations of aggregate materials.

颗粒大小和形态/形状特性可确保在运输应用中可靠且可持续地使用作为构造层放置的所有骨料骨架材料。这些骨料组件的组成和堆积在很大程度上取决于颗粒大小和形态学特性，这会影响车辆载荷下的层强度，模量和变形响应，因此有利于质量保证/质量控制（QA / QC）过程。迄今为止，针对尺寸和形状表征而开发的骨料成像系统主要集中在分离或轻微接触的骨料颗粒的测量上。迫切需要开发高效的计算机视觉算法，以基于图像的方式对密集堆积（或库存）的骨料进行评估，对于单个颗粒的大小和形态特性，这需要对库存进行图像分割。本文提出了一种基于深度学习技术的自动分类和堆积物聚集图像形态分析的创新方法。根据从伊利诺伊州各个采石场收集的图像建立特定任务的库存聚集图像数据集。在与颗粒位置和区域相关的每个图像上，手动标记来自库存图像的单个颗粒。然后使用称为Mask R-CNN的最新对象检测和分割框架来训练图像分割内核，该内核可实现用户独立的库存聚合图像分割。分割结果表明与地面真相标记具有良好的一致性，并提高了对密集堆叠和重叠的粒子图像进行大小和形态分析的效率。基于提出的方法，库存骨料图像分析有望成为骨料现场和现场评估的有效和创新应用。