A deep learning method for nanoparticle size measurement in SEM images

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 F1-score values of 87.79% and 93.50%, respectively, on the test set. The Spearman coefficient between the measured particle sizes and manual annotations was 0.91, with a mean relative error of 4.25%, confirming the accuracy and robustness of the method. This study presents a highly reliable automated method for nanoparticle size measurement, providing an effective tool for nanoparticle analysis and engineering applications.

Fig. 1. (a) The proposed network (the decoder of the network includes two CRCRA modules at its end, with the final CRCRA module omitting the concat operation); (b) the structure of Bottleneck; (c) the structure of CRCRA.

Fig. 2. The structure of CBAM.

Fig. 3. Overall flowchart of nanoparticle size measurement.

Fig. 4. Segmentation results comparison with and without the CRCRA module embedded in the network.

Fig. 5. Particle segmentation comparison between the proposed model and other models.

Fig. 6. Comparison between model and manual particle size measurements.