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通过微妙的缺陷增强和背景抑制进行光伏电池表面缺陷检测

Yange Sun¹, Guangxu Huang¹, Chenglong Xu¹

¹School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

|September 27, 2025

概括

这项研究引入了一种新型的光伏电池表面缺陷检测器 (PSDD),以准确识别光伏 (PV) 电池中的微妙缺陷. 新方法显著提高了缺陷检测的准确性,提高了太阳能转换效率.

科学领域:

材料科学材料科学材料科学
电气工程电气工程
计算机视觉计算机视觉

背景情况:

光伏 (PV) 电池对于太阳能至关重要,但制造缺陷如裂和中断降低了效率.
现有的缺陷检测方法与微妙的表面缺陷和背景噪音作斗争.
准确的缺陷识别对于优化光伏电池性能和可靠性至关重要.

研究的目的:

开发一款先进的光伏电池表面缺陷检测器 (PSDD),用于精确识别和定位微妙缺陷.
为了增强细微缺陷的特征表示,并在检测过程中抑制背景噪声.
为了提高光伏电池缺陷检测的整体准确性和稳定性.

主要方法:

提出了一种光伏电池表面缺陷探测器 (PSDD),其中包含一个微妙特征精制模块 (SFRM) 和一个背景噪声抑制块 (BNSB).
通过重新排列空间信息和使用注意力机制来突出与缺陷相关的道,SFRM可以改进细粒度的特征.
BNSB采用双路径策略,使用背景感知模块 (BAM) 和残余结构来实现多级特征融合和降噪.

主要成果:

拟议的PSDD在检测光伏电池的微妙表面缺陷方面取得了卓越的性能.
在PVEL-AD数据集中,PSDD显示了最高的mAP50得分93.6%,超过了现有方法.
集成的SFRM和BNSB模块有效地增强了特征表示,并抑制了背景噪声.

结论:

新型PSDD,其SFRM和BNSB组件,在光伏电池表面缺陷检测方面取得了重大进展.
这种方法有效地解决了微妙缺陷和复杂的背景噪声所带来的挑战.
改进的检测精度有可能提高光伏制造的质量控制和效率.

关键词:

注意力机制注意力机制深度学习方法深度学习方法光伏电池缺陷检测检测检测检测微妙的缺陷检测检测微妙的缺陷检测

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