An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.,A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.,Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.,First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

中文翻译：

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基于自适应深度特征和视觉显着性的太阳能电池微裂纹检测

一种有效的基于机器视觉的太阳能电池微裂纹检测方法可以经济地提高太阳能电池的合格率。然而,如何同时提取具有较强泛化能力和数据表示能力的特征对于基于机器视觉的方法来说仍然是一个悬而未决的问题。,本文提出了一种基于自适应深度特征和视觉显着性的微裂纹检测方法。 . 所提出的方法可以在没有任何监督训练的情况下从输入图像中自适应地提取深度特征。此外，考虑到微裂纹在人们看太阳能电池表面时可以明显吸引视觉注意力的事实，还引入了视觉显着性用于微裂纹检测。对现有的两个数据集进行综合评估，其中主观实验结果表明可以检测到大部分微裂纹，客观实验结果表明本研究提出的方法在检测精度方面具有更好的性能。首先，一种没有任何监督训练的自适应深度特征提取方案是建议用于微裂纹检测。其次，为微裂纹检测引入了视觉显着性。