ArticleRSC advances2025
A deep learning method for nanoparticle size measurement in SEM images.
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Multi-Source Porosity Image Normalization (NMI) in Selective Laser Melting for Reliable Reuse of Heterogeneous Microstructural Data.Materials (Basel, Switzerland) · 2025Article
- Green Synthesis of Chitosan Silver Nanoparticle Composite Materials: A Comparative Study of Microwave and One-Pot Reduction Methods.Polymers · 2025Article
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Authors and funding
3 authors.
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Abstract
Accurate characterization of nanoparticle size distribution is vital for performance modulation and practical applications. Nanoparticle size measurement in SEM images often requires manual operations, resulting in limited efficiency. Although existing semantic segmentation models enable automated measurement, challenges persist regarding small particle recognition, low-contrast region segmentation accuracy, and manual scalebar calibration needs. Therefore, we propose an improved U-Net model based on attention mechanisms and residual networks, combined with an automatic scalebar recognition algorithm, to enable accurate pixel-to-physical size conversion. The model employs ResNet50 as the backbone network and incorporates the convolutional block attention module (CBAM) module to enhance feature extraction for nanoparticles, especially small or low-contrast particles. The results show that the model achieved IoU and
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Registered trials
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