Photovoltaic cell panel grid line defect detection method and system based on artificial intelligence

Abstract

The invention provides a photovoltaic cell panel grid line defect detection method and system based on artificial intelligence, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring an image of a single photovoltaic cell panel; constructing an image matrix according to prior color features in the photovoltaic cell panel image, and performing sliding window calculation on the image matrix to obtain a single-channel image; obtaining a compression value of each column of pixels of the single-channel image as a one-dimensional sequence representing the photovoltaic cell panel image; and inputting the one-dimensional sequence into a time convolution network to obtain the periodicity of the grid line in the photovoltaic cell panel image, and judging the defect position of the grid line according to the abnormal point. According to the invention, the semantic segmentation network type is adopted to identify the content of each column in the image, the image processing mode does not need to debug parameters, the periodicity of the content of the photovoltaic cell panel is utilized to adapt to cell panel images of various sizes, the preprocessing process is simplified, and the generalization ability is high. According to the invention, the influence of camera parameters and illumination intensity can be eliminated, automation is realized, and the detection efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to an artificial intelligence-based detection method and system for photovoltaic cell grid line defects. Background technique [0002] Solar energy, as a renewable clean energy, has been used more and more in society. As the carrier of solar energy, photovoltaic panels are usually expected to have a long service life and high conversion rate, and in the manufacturing process Gridline defects appearing in , limit its applicability. Therefore, effective detection methods are very important to improve the quality of photovoltaic panels. [0003] Raster line detection usually uses the background modeling method and the frame difference method, but often requires a standard template. At the same time, the image processing method needs to artificially set more empirical thresholds, which makes the generalization ability of the system poor. The difference in strength will invalidat...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned technical problems, the present invention provides a method and system for detecting defects of photovoltaic cell grid lines based on artificial intelligence, and the adopted technical scheme is as follows:

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