Photovoltaic Cell Surface Defect Detection via Subtle Defect Enhancement and Background Suppression.

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL
Micromachines Pub Date : 2025-08-30 DOI:10.3390/mi16091003
Yange Sun, Guangxu Huang, Chenglong Xu, Huaping Guo, Yan Feng
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引用次数: 0

Abstract

As the core component of photovoltaic (PV) power generation systems, PV cells are susceptible to subtle surface defects, including thick lines, cracks, and finger interruptions, primarily caused by stress and material brittleness during the manufacturing process. These defects substantially degrade energy conversion efficiency by inducing both optical and electrical losses, yet existing detection methods struggle to precisely identify and localize them. In addition, the complexity of background noise and other factors further increases the challenge of detecting these subtle defects. To address these challenges, we propose a novel PV Cell Surface Defect Detector (PSDD) that extracts subtle defects both within the backbone network and during feature fusion. In particular, we propose a plug-and-play Subtle Feature Refinement Module (SFRM) that integrates into the backbone to enhance fine-grained feature representation by rearranging local spatial features to the channel dimension, mitigating the loss of detail caused by downsampling. SFRM further employs a general attention mechanism to adaptively enhance key channels associated with subtle defects, improving the representation of fine defect features. In addition, we propose a Background Noise Suppression Block (BNSB) as a key component of the feature aggregation stage, which employs a dual-path strategy to fuse multiscale features, reducing background interference and improving defect saliency. Specifically, the first path uses a Background-Aware Module (BAM) to adaptively suppress noise and emphasize relevant features, while the second path adopts a residual structure to retain the original input features and prevent the loss of critical details. Experiments show that PSDD outperforms other methods, achieving the highest mAP50 scores of 93.6% on the PVEL-AD.

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Abstract Image

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基于细微缺陷增强和背景抑制的光伏电池表面缺陷检测。
作为光伏发电系统的核心部件,光伏电池在制造过程中容易出现细微的表面缺陷,包括粗线、裂纹、断指等,主要是由应力和材料脆性引起的。这些缺陷通过诱导光和电损耗大大降低了能量转换效率,但现有的检测方法难以精确识别和定位它们。此外,背景噪声等因素的复杂性进一步增加了检测这些细微缺陷的难度。为了解决这些挑战,我们提出了一种新型的光伏电池表面缺陷检测器(PSDD),它可以在骨干网络和特征融合过程中提取细微缺陷。特别是,我们提出了一个即插即用的细微特征细化模块(SFRM),该模块集成到主干中,通过将局部空间特征重新排列到信道维度来增强细粒度特征表示,从而减轻下采样造成的细节损失。SFRM进一步采用一般关注机制自适应增强与细微缺陷相关的关键通道,提高细微缺陷特征的表征。此外,我们提出了一个背景噪声抑制块(BNSB)作为特征聚合阶段的关键组成部分,该块采用双路径策略融合多尺度特征,减少背景干扰,提高缺陷显著性。具体来说,第一种路径使用背景感知模块(BAM)自适应抑制噪声并强调相关特征,第二种路径采用残差结构保留原始输入特征并防止关键细节的丢失。实验表明,PSDD优于其他方法,在PVEL-AD上的mAP50得分最高,达到93.6%。
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来源期刊
Micromachines
Micromachines NANOSCIENCE & NANOTECHNOLOGY-INSTRUMENTS & INSTRUMENTATION
CiteScore
5.20
自引率
14.70%
发文量
1862
审稿时长
16.31 days
期刊介绍: Micromachines (ISSN 2072-666X) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to micro-scaled machines and micromachinery. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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