针对太阳能电池组件中电池片出现隐裂导致整片电池破碎,最终影响整个组件发电量的问题，在对电池组件光致发光（PL）图像待检测区域筛选定位的基础上，提出了一种利用卷积神经网络（CNN）进行电池组件隐裂缺陷检测的方法。首先利用PL成像方法获取电池组件图像，然后对图像进行预处理，基于聚类的方法对待检测目标区域进行筛选定位，最后利用3种不同结构的卷积神经网络模型对电池片进行缺陷检测，并进行准确率对比，使最优识别准确率达到99.25%。实验结果验证了该方法能准确地检测出太阳能电池组件的隐裂缺陷。
Abstract
Aiming at the problem that the cracked cell in the solar cell module eventually causes the whole cell to break and affect the power generation of the whole component, a method for detecting cracked defects of battery components using convolutional neural network network (CNN), is proposed based on the screening and positioning of the photoluminescence (PL) image of the battery component. The basic idea is to obtain the image of the battery component by using the PL detection technology first, then pre-process the image, filter and locate the target area based on the clustering method, and finally use three convolutional neural network models to detect the defect of the battery, and compare the accuracy. A large number of experimental results verify that the above method can accurately detect the cracking defects of solar cell modules.
关键词
卷积神经网络 /
图像识别 /
缺陷检测 /
光致发光
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Key words
convolutional neural network /
photoluminescence /
image recognition /
defects detection
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基金
国家重点专项资助项目（2018YFC1902400）；国家自然科学基金（51874217，51805386）；湖北省技术创新专项重大项目（2017ACA180）
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脚注
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