Accurate characterization of nanoparticle geometry and morphology is essential for understanding their structure–property relationships. However, in most electron microscopy images, nanoparticles are densely distributed and are often affected by strong background noise and particle overlap, making conventional manual analysis time-consuming and inefficient. To address this issue, this study proposes Nanoparticle Segmentation You Only Look Once (NSYOLO), an enhanced deep learning–based instance segmentation framework for the automatic and high-precision recognition and segmentation of nanoparticles in electron microscopy images. The framework is trained on a multi-type dataset comprising nanocubes, nanospheres, and nanorods, and introduces a boundary-aware dynamic snake convolution (BADSConv) module to enhance boundary feature representation, along with a bi-level routing attention (BRA) mechanism to improve global feature modeling. Experimental results demonstrate that NSYOLO increases mean Average Precision (mAP)@0.5 from 0.906 to 0.957 and outperforms open-source automated tools, such as ImageJ and ImageDataExtractor, particularly in images with complex backgrounds and overlapping particles. In addition, the NSYOLO-based analysis system is developed to enable automated nanoparticle segmentation, size statistics, and the generation of editable Word reports without requiring any programming experience, thereby providing an efficient, reliable, and user-friendly solution for high-throughput nanoparticle morphology analysis.