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.
This work is supported by the National Science Foundation of China under Grants (No: 61501407, 61603350, 61672471, 61873246), Key Science and Technology Program of Henan Province (202102210347, 202102210143), Key research project of Henan Province Universities (No: 19A413014), Plan For Scientific Innovation Talent of Henan Province (No: 184200510010), Center Plain Science and Technology Innovation Talents(No. 194200510016), Science and Technology Innovation Team Project of Henan Province University (No. 19IRTSTHN013), Doctor fund project of Zhengzhou University of Light Industry (No: 2014BSJJ016).
Qian, X., Li, J., Zhang, J., Zhang, W., Yue, W., Wu, Q.-E., Zhang, H., Wu, Y. and Wang, W. (2020), "Micro-crack detection of solar cell based on adaptive deep features and visual saliency", Sensor Review, Vol. 40 No. 4, pp. 385-396. https://doi.org/10.1108/SR-05-2019-0124Download as .RIS
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