A Convolutional Neural Network Based Defect Identification Method for Solar Panels

Abstract

The invention relates to a defect identification method for a solar panel based on a convolution neural network (CNN). The method comprises the two stages of model off-line training and on-line detection. CNN models are applied to defect identification of the solar panel, and defect detection and classification are progressively realized by two CNN models. Firstly, a CNN binary classification model is used for distinguishing qualified and defective images, and then a CNN multi-classification model is used for classifying images which are classified as defects by the binary classification model. The CNN models adopt the same processing flow for various defect types of the solar panel, namely, feature extraction and feature classification are performed rapidly and automatically through iterative training. For a new defect type, detection of the defect type can be realized by only collecting sample data of the defect type, adding the sample data into a training data set and training the models. Through adoption of the defect identification method, the location of a small defective solar panel can be identified at relatively high accuracy. Moreover, the method can classify various defects, so that the applicability of the method is wider.

Description

technical field [0001] The invention relates to the technical field of defect detection of solar cell panels, in particular to a method for identifying defects of solar cell panels based on a convolutional neural network. Background technique [0002] Solar energy is a clean energy source. Due to the complex production process of solar panels, coupled with the artificial factors in the production, transportation and installation process, the panels are prone to various defects, which increases the damage rate of the panels, and these defects will seriously reduce the photoelectric conversion of the panels. efficiency and service life. Therefore, it is very important to detect panel defects during the production process. At present, the electroluminescence (EI) image of the battery panel is mainly detected. However, the texture structure of the panel surface and the impurities of the polysilicon material bring great difficulties to defect detection. At present, there are ...

AI Technical Summary

Problems solved by technology

This method uses the simple statistical feature of the gray difference between the sample image and the template image to identify defects, and cannot effectively detect defects with impurity interference and various shapes.

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