Clustering of Weibo users is one of the most important topics in data mining

Crack extraction of solar panels has become a research focus in recent years. The cracks are small and hidden. In addition, there are particles of irregular shape and size on the surface of the polycrystalline solar panel, whose reflection position and direction are random. Therefore, there is a complex and uneven texture background on the solar panel image, which makes the crack extraction more difficult. In this paper, a crack extraction method combining image texture and morphological features is proposed. Firstly, the background texture and multi-scale details are suppressed by the linear filter and the Laplace pyramid decomposition method. Secondly, the edge can be extracted based on the modulus maximum method of the wavelet transform. Finally, cracks were extracted by using the improved Fuzzy C-means (FCM) clustering combining the morphological and texture features of the cracks. To make the extraction results more accurate and reasonable, an improved region growth algorithm is proposed to optimize the extraction results. All of the above research is closely centered on the accuracy and stability requirements of the solar cell crack detection, which is also the key point of this paper. The experimental results show that various improved or innovative algorithms proposed in this paper can accurately extract the position of cracks and obtain better extraction results. The detection results have good stability and can be faithful to the actual situation, which will promote the application of solar cells in more fields.

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