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Scanning Electron Microscopy

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
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LBMS-SAM: Segment anything model guided SEM image segmentation for lithium battery materials.

Yu Qi¹, Jun Zhang², Jian Kuang¹

¹University of Science and Technology of China, Hefei 230026, China; Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; High Magnetic Field Laboratory of Anhui Province, Hefei 230031, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 25, 2025

Summary

This study introduces LBMS-SAM, an AI model for lithium battery material quality inspection using SEM images. It automates particle size analysis, improving accuracy and efficiency over manual methods.

Keywords:

Image segmentation Lithium battery Segment anything model Transformer

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Area of Science:

Materials Science
Artificial Intelligence
Image Analysis

Background:

Quality inspection of lithium battery materials relies on manual analysis of particle sizes in SEM images.
Manual annotation is time-consuming, labor-intensive, and susceptible to subjective errors.
Automating this process is crucial for improving efficiency and accuracy in material quality control.

Purpose of the Study:

To develop an automated artificial intelligence solution for lithium battery material quality inspection.
To address the limitations of manual annotation in analyzing particle sizes from SEM images.
To introduce a novel deep learning model for SEM image segmentation tasks.

Main Methods:

A new dataset, the LBMS dataset, was created specifically for lithium battery material SEM image segmentation (LBMS).
A specialized model, LBMS-SAM, was proposed, incorporating a Gabor and Sobel edge feature extraction module (GSEFE).
A multi-layer denoised features fusion module (MDFF) using wavelet transform was designed to enhance feature extraction and reduce noise.

Main Results:

The proposed LBMS-SAM model demonstrated superior performance on the LBMS dataset.
LBMS-SAM outperformed existing state-of-the-art (SOTA) methods across all evaluation metrics.
The model achieved accurate extraction of edge information and efficient fusion of global contextual features with minimal added parameters.

Conclusions:

The developed LBMS-SAM model offers an effective and automated solution for lithium battery material quality inspection.
The AI-driven approach significantly enhances accuracy and efficiency compared to traditional manual methods.
This work paves the way for advanced automated quality control in battery material manufacturing.