基于Mamba和卷积神经网络的太阳电池缺陷检测方法

钱尧; 刘刚; 赵龙; 唐建超

doi:doi:10.19912/j.0254-0096.tynxb.2024-1988

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于Mamba和卷积神经网络的太阳电池缺陷检测方法

更新时间：2026-04-08

- 基于Mamba和卷积神经网络的太阳电池缺陷检测方法
- 基于Mamba和卷积神经网络的太阳电池缺陷检测方法
- 太阳能学报 2026年47卷第3期页码：690-696
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金（62203224）;上海市地方能力建设项目（22010501300）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1988
  中图分类号： TP183
- 网络首发：2026-04-07，
  
  纸质出版：2026
- 稿件说明：
移动端阅览
钱尧, 刘刚, 赵龙, 等. 基于Mamba和卷积神经网络的太阳电池缺陷检测方法[J]. 太阳能学报, 2026,47(3):690-696.

钱尧, 刘刚, 赵龙, et al. 基于Mamba和卷积神经网络的太阳电池缺陷检测方法[J]. 2026, 47(3): 690-696.
钱尧, 刘刚, 赵龙, 等. 基于Mamba和卷积神经网络的太阳电池缺陷检测方法[J]. 太阳能学报, 2026,47(3):690-696. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1988.

钱尧, 刘刚, 赵龙, et al. 基于Mamba和卷积神经网络的太阳电池缺陷检测方法[J]. 2026, 47(3): 690-696. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1988.

摘要

针对太阳电池电致发光图像中缺陷种类复杂、人工检测效率低的问题

提出一种基于Mamba与卷积神经网络的缺陷检测方法。该方法通过融合卷积神经网络的局部特征提取能力与Mamba结构的全局信息捕获优势

克服了单一架构在复杂场景中的局限性。Mamba结构引入轻量级注意力机制

动态调节不同层次特征的权重

增强全局信息感知能力。结合卷积神经网络后

该模型既能提取局部细节特征

又能高效融合全局上下文信息

从而显著提升了缺陷检测的精度与性能。经过大量实验证明

所提方法具有模型参数量小、检测精度高和鲁棒性强等优点

显著提升了太阳电池的缺陷检测效果。

Abstract

In order to address the problems of complex defect types and low efficiency of manual detection in solar cell electroluminescence images

a defect detection method based on Mamba and convolutional neural networks is proposed. The method overcomes the limitations of a single architecture in complex scenarios by fusing the local feature extraction capability of convolutional neural networks with the global information capture advantage of Mamba structures. The Mamba structure introduces a lightweight attention mechanism that dynamically adjusts the weights of features at different levels to enhance global information perception capability. Combined with convolutional neural networks

the model can both extract local detailed features and efficiently fuse global contextual information

thus significantly improving the accuracy and performance of defect detection. After a large amount of experiments

it is proved that the proposed method has the advantages of a low number of model’s parameters

high detection accuracy and strong robustness

which significantly improves the defect detection performance of solar cells.

关键词

Keywords

references

LI G Q, AKRAM M W, JIN Y, et al.Thermo-mechanical behavior assessment of smart wire connected and busbarPV modules during production, transportation, and subsequent field loading stages[J]. Energy, 2019, 168: 931-945.

PAGGI M, BERARDONE I, INFUSO A, et al.Fatigue degradation and electric recovery in Silicon solar cells embedded in photovoltaic modules[J]. Scientific reports, 2014, 4: 4506.

TOMÁNEK P, ŠKARVADA P, MACKŮ R, et al. Detection and localization of defects in monocrystalline silicon solar cell[J]. Advances in optical technologies, 2010, 2010: 805325.

OSAWA S, NAKANO T, MATSUMOTO S, et al.Fault diagnosis of photovoltaic modules using AC impedance spectroscopy[C]//2016 IEEE International Conference on Renewable Energy Research and Applications(ICRERA). Birmingham, UK, 2017: 210-215.

HE Y Z, DU B L, HUANG S D.Noncontact electromagnetic induction excited infrared thermography for photovoltaic cells and modules inspection[J]. IEEE transactions on industrial informatics, 2018, 14(12): 5585-5593.

DEITSCH S, CHRISTLEIN V, BERGER S, et al.Automatic classification of defective photovoltaic module cells in electroluminescence images[J]. Solar energy, 2019, 185: 455-468.

EBNER R, ZAMINI S, UJVARI G.Defect analysis in different photovoltaic modules using electroluminescence (EL) and infrared (IR)-thermography[C]//25th European Photovoltaic Solar Energy Conference and Exhibition. Valencia, Spain, 2010: 333-336.

KÖNTGES M, SIEBERT M, HINKEN D, et al. Quantitative analysis of PV-modules by electroluminescence images for quality control[C]//Proceedings of the 24th European Photovoltaic Solar Energy Conference. Hamburg, Germany, 2009: 21-24.

彭自然, 张颖清, 肖伸平, 等. 基于YOLOv5的太阳电池表面缺陷检测[J]. 太阳能学报, 2024, 45(6): 368-375.

刘涛, 孙会, 卞佰成, 等. 多尺度特征融合的太阳电池表面缺陷检测[J]. 太阳能学报, 2025, 46(2): 342-349.

AKRAM M W, LI G Q, JIN Y, et al.CNN based automatic detection of photovoltaic cell defects in electroluminescence images[J]. Energy, 2019, 189: 116319.

TANG W Q, YANG Q, XIONG K X, et al.Deep learning based automatic defect identification of photovoltaic module using electroluminescence images[J]. Solar energy, 2020, 201: 453-460.

XIE X Y, LAI G Z, YOU M Y, et al.Effective transfer learning of defect detection for photovoltaic module cells in electroluminescence images[J]. Solar energy, 2023, 250: 312-323.

FUKUSHIMA K.Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J]. Biological cybernetics, 1980, 36(4): 193-202.

SU B Y, ZHOU Z, CHEN H Y.PVEL-AD: a large-scale open-world dataset for photovoltaic cell anomaly detection[J]. IEEE transactions on industrial informatics, 2023, 19(1): 404-413.

LIU Z, LIN Y T, CAO Y, et al.Swin transformer: hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada, 2022: 9992-10002.

LIU Y, TIAN Y, ZHAO Y et al. Vmamba: visual state space model[C]//Advances in Neural Information Processing Systems 37. Vancouver, Canada, 2024: 103031-103063.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于多尺度渐近金字塔的太阳电池缺陷检测网络

基于T-VGG的太阳电池片缺陷检测

基于迁移学习与ResNet的太阳电池缺陷检测方法

基于改进YOLOX-s算法的航天太阳电池缺陷检测

基于ICPO优化VMD耦合深度学习模型的中短期风电功率预测