Abstract Manufacturing process defects or artificial operation mistakes may lead to solar cells having surface cracks, over welding, black edges, unsoldered areas, and other minor defects on their surfaces. These defects will reduce the efficiency of solar cells or even make them completely useless. In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks. The detection results from two different convolution neural networks, i.e., Faster R-CNN and R-FCN, are combined to improve detection precision and position accuracy. In addition, according to the inherent characteristics of the surface defects in solar cells, two other strategies are used to further improve the detection performance. First, the anchor points of the region proposal network (RPN) are set by adding multi-scale and multi-aspect regions to overcome the problem of high false negative rate caused by the limitation of anchor points. Second, in view of the subtle and concealed defects of solar cells, the hard negative sample mining strategy is used to solve the problem of low detection precision caused by the negative sample space being too large. The experimental results showed that the proposed method effectively reduced the false negative rate and the false positive rate of a single network, and it greatly improved the accuracy of the locations of defects while improving the object recall rate.

中文翻译：

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通过融合多通道卷积神经网络检测太阳能电池表面缺陷

摘要 制造工艺缺陷或人为操作失误可能导致太阳能电池表面出现裂纹、过焊、黑边、未焊区等细微缺陷。这些缺陷会降低太阳能电池的效率，甚至使它们完全无用。本文提出了一种融合多通道卷积神经网络的太阳能电池表面缺陷检测算法。Faster R-CNN和R-FCN两种不同卷积神经网络的检测结果相结合，提高检测精度和定位精度。此外，根据太阳能电池表面缺陷的固有特性，采用了另外两种策略来进一步提高检测性能。第一的，区域提议网络（RPN）的锚点是通过添加多尺度、多方面的区域来设置的，以克服锚点限制导致的高误报率的问题。其次，针对太阳能电池细微隐蔽的缺陷，采用硬负样本挖掘​​策略，解决负样本空间过大导致检测精度低的问题。实验结果表明，所提出的方法有效降低了单个网络的假阴性率和假阳性率，在提高对象召回率的同时大大提高了缺陷定位的准确性。针对太阳能电池细微隐蔽的缺陷，采用硬负样本挖掘​​策略，解决负样本空间过大导致检测精度低的问题。实验结果表明，所提出的方法有效降低了单个网络的假阴性率和假阳性率，在提高对象召回率的同时大大提高了缺陷定位的准确性。针对太阳能电池细微隐蔽的缺陷，采用硬负样本挖掘​​策略，解决负样本空间过大导致检测精度低的问题。实验结果表明，所提出的方法有效降低了单个网络的假阴性率和假阳性率，在提高对象召回率的同时大大提高了缺陷定位的准确性。