基于改进Unet网络的锂电池极片表面缺陷检测

陈晓荣; 杨雪荣; 成思源; 刘国栋

doi:10.12052/gdutxb.220161

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基于改进Unet网络的锂电池极片表面缺陷检测

计算机科学与技术 | 更新时间：2026-05-27

- 基于改进Unet网络的锂电池极片表面缺陷检测
- Surface Defect Detection of Lithium Battery Electrodes Based on Improved Unet Network
- 广东工业大学学报 2023年40卷第4期页码：60-66, 93
- 作者机构：
  
  广东工业大学机电工程学院,广东,广州,510006
- 作者简介：
  
  陈晓荣 (1998–)，男，硕士研究生，主要研究方向为虚拟现实
  杨雪荣(1978–)，女，副教授，博士，硕士生导师，主要研究方向为逆向工程技术、计算机视觉检测技术，E-mail： yxrlyl@163.com
- 基金信息：
  
  广东省高等教育教学改革项目(粤教高函[2020]20号);广东省研究生教育创新计划项目(2021JGXM043);校级“本科教学工程”项目（广工大教字[2020]22号）
- DOI：10.12052/gdutxb.220161
  中图分类号： TP391.41;TM912
- 网络首发：2023-07-25，
  
  纸质出版：2023
- 稿件说明：
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陈晓荣, 杨雪荣, 成思源, 刘国栋. 基于改进Unet网络的锂电池极片表面缺陷检测[J]. 广东工业大学学报, 2023, 40(4): 60-66, 93.

Chen Xiao-rong, Yang Xue-rong, Cheng Si-yuan, et al. Surface Defect Detection of Lithium Battery Electrodes Based on Improved Unet Network[J]. 2023, 40(4): 60-66, 93.
陈晓荣, 杨雪荣, 成思源, 刘国栋. 基于改进Unet网络的锂电池极片表面缺陷检测[J]. 广东工业大学学报, 2023, 40(4): 60-66, 93. DOI： 10.12052/gdutxb.220161.

Chen Xiao-rong, Yang Xue-rong, Cheng Si-yuan, et al. Surface Defect Detection of Lithium Battery Electrodes Based on Improved Unet Network[J]. 2023, 40(4): 60-66, 93. DOI： 10.12052/gdutxb.220161.

摘要

为避免锂电池由于极片表面缺陷导致寿命缩短或出现安全事故等问题，需要研究高效准确的锂电池极片缺陷检测方法。本文使用结构简单的Unet语义分割网络对锂电池进行缺陷分割。为提高分割精度，首先用与Unet编码结构相似的VGG16替换原网络中的编码结构，以获取训练好的预训练权重；然后将简易的融合金字塔网络(Simply Fusion Pyramid Network，SFPN)的特征融合模块加入Unet网络的跳跃连接上，避免特征图之间出现较大的信息差异；最后运用标签平滑来优化损失函数，防止网络出现过拟合现象。通过实验验证，本文方法优化后的语义分割网络准确率提升至93.70%，误分割、分割不连续等现象出现概率明显降低，该优化流程具有一定实用价值。

Abstract

In order to avoid the problems of life shortening or safety accidents caused by the surface defects of lithium batteries

it is necessary to study an efficient and accurate methods for lithium battery electrode plate defect detection. In this research

the simple Unet semantic segmentation network is used to detect defects of lithium battery. In order to improve the segmentation accuracy

first the coding structure in the original network is replaced with VGG16

which is similar to the Unet coding structure

to obtain the pre-training weights had been trained. Then

feature fusion module of the simply fusion pyramid network (SFPN) is added to the skip connection of the Unet network to avoid large information differences between feature maps. Finally

label smoothing is applied to optimize the loss function to prevent the network from overfitting. Through experimental verification

the accuracy of the semantic segmentation network optimized by the proposed method is improved to 93.70%

and the probability of false segmentation and segmentation discontinuity is significantly reduced. This optimization process has certain practical value.

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Keywords

references

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