Parameter measurement of nanoparticle, which aims at evaluation of the quality of nanomaterials, is essential to nanotechnology and many applications. According to the nanoparticle images captured by transmission electron microscopy, this paper presents an automated procedure that can expedite the parameter measurement of the rodlike nanoparticles. Nanoparticle segmentation is the most important step in nanoparticle parameter measurement. The challenge of this task involves segmenting the adhesive nanoparticles and nanoparticles with weak contours. To accurately measure nanoparticle size and evaluate nanomaterial quality, firstly, according to the characteristics of agglomeration and adhesion of nanoparticle images, the Mask R-CNN network was selected to segment the nanoparticle images, and the network was optimized to improve the segmentation accuracy. Secondly, according to the particle segmentation result, the minimum circumscribed rectangle of the rodlike nanoparticle boundary is obtained. Finally, the size and shape parameters of the particles are measured based on the minimum circumscribed rectangle. The experimental results confirm the effectiveness of the proposed method for measuring the rodlike nanoparticle parameters.

纳米粒子的参数测量旨在评估纳米材料的质量，对纳米技术和许多应用至关重要。根据透射电子显微镜捕获的纳米颗粒图像，本文提出了一种自动化程序，可以加快棒状纳米颗粒的参数测量。纳米颗粒分割是纳米颗粒参数测量中最重要的步骤。该任务的挑战涉及分割粘合剂纳米颗粒和轮廓较弱的纳米颗粒。为了准确地测量纳米粒子的尺寸并评估纳米材料的质量，首先，根据纳米粒子图像的团聚和粘附特性，选择Mask R-CNN网络对纳米粒子图像进行分割，对网络进行了优化，以提高分割精度。其次，根据颗粒分割结果，获得棒状纳米颗粒边界的最小外接矩形。最后，基于最小外接矩形测量粒子的大小和形状参数。实验结果证实了所提出的用于测量棒状纳米颗粒参数的方法的有效性。