Abstract
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.
Highlights
With the continuous exploitation and consumption of traditional energy, the energy crisis has created huge global challenges
Samir et al proposed an extraction algorithm based on particle swarm optimization (PSO) by analyzing the gradient of the crack in solar panels [7]
This method first extracts the edges of the image based on the PSO, analyzes the features of crack and finger to extract their feature vectors, and completes the crack extraction by classification
Summary
With the continuous exploitation and consumption of traditional energy, the energy crisis has created huge global challenges. Samir et al proposed an extraction algorithm based on particle swarm optimization (PSO) by analyzing the gradient of the crack in solar panels [7]. This method first extracts the edges of the image based on the PSO, analyzes the features of crack and finger to extract their feature vectors, and completes the crack extraction by classification. JinSeok et al proposed using local means to detect defects [10] In this method, the solar panel image is divided into many small pieces, and the binary mean value of each piece is calculated, respectively.
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