This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.
A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.
Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.
First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.
This work is supported by the National Science Foundation of China under Grants (No: 61501407, 61503173, 61603350), National 973 Program (No: 613237), Major Science and Technology Projects of Henan Province (No: 161100211600), Henan Province Outstanding Youth on Science and Technology Innovation (No: 164100510017), Key research project of Henan Province Universities (No: 15A413006), Key Science and Technology Program of Henan Province (No: 172102210062), Doctor fund project of Zhengzhou University of Light Industry (No: 2014BSJJ016, 2016BSJJ002, 2016BSJJ006).
Qian, X., Zhang, H., Yang, C., Wu, Y., He, Z., Wu, Q.-E. and Zhang, H. (2018), "Micro-cracks detection of multicrystalline solar cell surface based on self-learning features and low-rank matrix recovery", Sensor Review, Vol. 38 No. 3, pp. 360-368. https://doi.org/10.1108/SR-08-2017-0166Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited