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首页> 外文期刊>IEEE transactions on industrial informatics >Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network
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Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network

机译：基于深度学习的太阳能电池制造缺陷检测与互补注意网络

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摘要

The automatic defects detection for solar cell electroluminescence (EL) images is a challenging task, due to the similarity of defect features and complex background features. To address this problem, in this article a novel complementary attention network (CAN) is designed by connecting the novel channel-wise attention subnetwork with spatial attention subnetwork sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features. In CAN, the novel channel-wise attention subnetwork applies convolution operation to integrate the concatenated and discriminative output features extracted by global average pooling layer and global max pooling layer, which can make fully use of these informative features. Furthermore, a region proposal attention network (RPAN) is proposed by embedding CAN into region proposal network in faster R-CNN (convolution neutral network) to extract more refined defective region proposals, which is used to construct a novel end-to-end faster RPAN-CNN framework for detecting defects in raw EL image. Finally, some experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method performs much better than other methods in terms of defects classification and detection results in raw solar cell EL images.
机译：由于缺陷特征和复杂的背景特征的相似性，太阳能电池电致发光（EL）图像的自动缺陷检测是一个具有挑战性的任务。为了解决这个问题，在本文中，通过顺序地将新的通道 - 方向关注子网连接到空间关注子网络，通过互补地抑制背景噪声特征并突出显示背景噪声特征，通过采用互补来突出缺陷特征来设计一种新的互补注意网络（CAN）。通道特征和空间位置特征的优点。在CAN中，新颖的频道明智的子网络应用卷积操作，以集成全局平均池和全局最大池层提取的连接和辨别输出功能，可以充分利用这些信息特征。此外，在更快的R-CNN（卷积中性网络）中将CAN嵌入到区域提案网络中提出了一个区域提案网络（RPAN）以提取更精细的有缺陷区域提出，该建议用于构建新的端到端RPAN-CNN检测原始EL图像缺陷的框架。最后，在包括3629个图像的大型EL数据集上的一些实验结果，其中2129个是有缺陷的，表明该方法在原始太阳能电池EL图像中的缺陷分类和检测结果方面比其他方法更好地执行。

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