电致发光(Electroluminescence, EL)下的光伏电池EL图像背景表现为复杂的非均匀纹理特征，且存在与裂纹相似的晶粒伪缺陷，同时裂纹表现为形状多样的多尺度特征，以上难点为检测任务带来了极大的挑战。因此，本文提出融合注意力的多尺度Faster-RCNN模型，一方面，采用改进的特征金字塔网络获取多尺度的高级语义特征图，以此来提高网络对多尺度裂纹缺陷的特征表达能力。另一方面，采用改进的注意力区域推荐网络A-RPN，提高模型对裂纹缺陷的关注并抑制复杂背景及晶粒伪缺陷的特征。同时，在RPN网络训练过程中，采用损失函数Focal loss，以此来降低训练过程中简单样本所占比重，使其更加关注难以区分的样本。实验结果表明，改进的算法使得EL图像裂纹缺陷检测的准确率提高，达到接近95%。
融合注意力的多尺度Faster RCNN的裂纹检测
作者单位信息
出版日期：2021年1月15日
摘要
参考文献
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国家自然科学基金资助项目(61873315)
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陈海永, 赵鹏, 闫皓炜. 融合注意力的多尺度Faster RCNN的裂纹检测[J]. 光电工程, 2021, 48(1): 200112.
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