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Updated: Jan 11, 2026

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

Published on: August 26, 2015

A real time segmentation network for lithium battery surface defect detection.

Jiaxing Xie^1,2,3,4, Peiwen Wu⁵, Jiasi Chen⁵

¹College of Electronic Engineering (College of AI), South China Agricultural University, Guangzhou, 510642, China. xjx1998@scau.edu.cn.

Scientific Reports

|November 19, 2025

Summary

This summary is machine-generated.

This study introduces the Dual Attention Pyramid Segmentation Network (DAPSeg) for lithium battery surface defect detection. DAPSeg effectively identifies tiny defects and balances high accuracy with real-time performance.

Keywords:

Attention mechanism Encoder–decoder Feature enhancement Multi-scale feature fusion Stable diffusion Surface defect detection

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Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

Published on: March 7, 2018

Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Lithium battery surface defect detection is crucial for industrial applications.
Existing methods struggle with varying defect scales, especially tiny defects.
High accuracy and real-time performance are essential for defect detection.

Purpose of the Study:

To propose a novel network, Dual Attention Pyramid Segmentation Network (DAPSeg), for precise and real-time lithium battery surface defect segmentation.
To address challenges of scale variation and the need for simultaneous high accuracy and speed.

Main Methods:

Developed DAPSeg featuring a Selective Kernel Module (SKM) for adaptive multi-scale feature extraction.
Employed a lightweight segmentation head with Blueprint Separable Layer (BSL) and Dual Attention Feature Fusion Module (DAFFM).
Utilized a diffusion model for data augmentation on the LB-SD dataset to mitigate overfitting.

Main Results:

DAPSeg achieved mIoU scores of 79.57% (LB-SD), 83.53% (MT), and 89.10% (MSD).
The model demonstrated a processing speed of 74.09 FPS.
Outperformed state-of-the-art models in balancing accuracy and inference speed.

Conclusions:

DAPSeg offers a robust solution for lithium battery surface defect detection.
The network achieves high precision and real-time processing capabilities.
DAPSeg exhibits strong generalization performance across different datasets.