Photovoltaic module unsupervised defect detection method based on GAN improved algorithm

Abstract

The invention relates to a photovoltaic module unsupervised defect detection method based on a GAN improved algorithm. The method comprises the following steps: step 1, training a generative adversarial network module according to an SSIM-GAN algorithm model; 2, training an encoder network module according to the SSIM-GAN algorithm model; 3, constructing a defect discrimination module; step 4, carrying out image detection. The method provided by the invention has the beneficial effects that the method can effectively detect tiny and diverse defects of the photovoltaic module, and can solve theproblem of unbalanced samples; an SSIM-GAN model is constructed, and the difference between the images is described by using structural similarity; a normal image is generated through a generative adversarial network; the images are mapped to the corresponding hidden spaces through the encoder network, whether the images to be detected have defects or not is judged through the defect detection module, rapid and accurate detection of unknown defects can be achieved under the condition that the number of the defect images is small, and the method has the advantages of being high in environmental adaptability and robustness.

Description

technical field [0001] The invention belongs to the field of production inspection of industrial products, and in particular relates to an unsupervised defect inspection method for photovoltaic modules based on an improved GAN algorithm. Background technique [0002] Due to the depletion of traditional energy sources, new energy sources represented by solar photovoltaic power generation have developed rapidly in recent years. In the production and processing of photovoltaic cell components, in addition to the defects of the material itself, the multiple processing of the cells on the automated production line will also increase the damage rate of the cells, resulting in hidden cracks, debris, virtual welding, and broken grids. These defects directly affect the conversion efficiency and service life of the product. With the continuous improvement of the level of industrial automation, traditional manual detection is no longer suitable for the current automated production env...

AI Technical Summary

Problems solved by technology

For photovoltaic crack defects, the defects are not obvious. The existing deep learning algorithms often require samples with a balanced distribution, and can only detect larger objects, and have poor detection capabilities for small anomalies.

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