首页|基于改进轻量化U-Net模型的光伏电池EL图像缺陷检测

基于改进轻量化U-Net模型的光伏电池EL图像缺陷检测

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基于实际工程检测现场神经网络结构庞大、参数量巨大、环境复杂,硬件设备性能差等原因导致缺陷的实时检测速率慢、精度低的问题,本研究结合MobileNet中的深度可分离卷积配合ECA注意力机制模块的轻量化思想,以及U-Net网络的特征提取模型提出了一种基于改进U-Net网络模型的光伏电池板缺陷检测方法.同时,根据光伏电池缺陷的特点,选择适合的激活函数以及对交叉熵损失函数进行了改进.实验结果表明,改进的U-Net算法较原算法不仅将参数量减少了36％,而且对裂纹、黑斑等缺陷的检测精度达到了97.05％,相对传统网络具有较好的光伏电池表面缺陷分割效果.
Defect detection of photovoltaic cell EL images based on improved lightweight U-Net model
Due to the large neural network structure,vast number of parameters,complex environment,and poor performance of hardware equipment in actual engineering inspection sites,the real-time detection rate of defects is slow and the accuracy is low. This study combines the lightweight concept of Depthwise Separable Convolution from MobileNet with the ECA attention mechanism module,as well as the feature extraction model of the U-Net network,to propose a photovoltaic panel defect detection method based on an improved U-Net network model. At the same time,according to the characteristics of photovoltaic cell defects,suitable activation functions were selected and the cross-entropy loss function was improved. Experimental results show that the improved U-Net algorithm not only reduced the number of parameters by 36％ compared to the original algorithm,but also achieved a detection accuracy of 97.05％ for defects such as cracks and black spots,demonstrating better performance in segmenting surface defects of photovoltaic cells than traditional networks.

electroluminescencephotovoltaic celldefect detectiondepthwise separable convolutionU-NetECAimage segmentation

汪方斌、李文豪

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安徽建筑大学机械与电气工程学院 合肥 230601

安徽建筑大学建筑机械故障诊断与预警重点实验室 合肥 230601

电致发光 光伏电池 缺陷检测 深度可分离卷积 U-Net ECA 图像分割

安徽省自然科学基金安徽省教育厅高校自然科学重点项目安徽省教育厅高校研究生科学研究项目安徽省教育厅高校协同创新项目安徽省住房城乡建设科学技术计划项目安徽省住房城乡建设科学技术计划项目安徽省住房城乡建设科学技术计划项目安徽省高等学校科学研究项目

2008085UD09KJ2020A0487YJS20210512GXXT-2021-0102022-YF0162022-YF0652023-YF0502022AH040044

2024

10.19651/j.cnki.emt.2415314

电子测量技术
北京无线电技术研究所

电子测量技术

CSTPCD北大核心
影响因子：1.166
ISSN：1002-7300
年,卷(期)：2024.47(5)
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