Solar cell broken gate defect detection method based on convolutional neural network

Abstract

The invention relates to the technical field of image processing and computer vision and particularly relates to a solar cell broken gate defect detection method based on a densely connected convolutional neural network. The method comprises a step of dividing an acquired polysilicon solar cell broken gate defect image into a training set and a test set, a step of extracting an interested target candidate region by horizontal integral projection and training a two-class classifier based on the densely connected convolutional convolution neural network through the training set, a step of sending the test set into the trained classifier to classify and detecting a defect area, and a step of calculating a communication area and removing a detected discrete position according to the scale distribution characteristics of the broken gate defect and drawing a circumscribed rectangle of the defect position to obtain a detection result. According to the method, the structure of the densely connected convolutional convolution neural network is used to train the image block classifier for the first time to realize defect detection, the defect image can be accurately detected in a complex anddiverse background, the position of the defect area is given, and the automatic monitoring of product quality is completed.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to a detection method for solar battery segment grid defects based on a densely connected convolutional neural network. Background technique [0002] The broken grid defect in the solar cell will not only reduce the service life of the cell but also affect the conversion efficiency of the solar cell for photoelectric conversion, so the defect detection of the solar cell is an important step in the quality control of the solar cell. Defects are not easy to be found by human eyes under visible light conditions, and electroluminescence imaging technology can help highlight defect areas, so it is widely used in solar cell imaging. In recent years, with the maturity of machine vision technology, more and more manufacturing enterprises have begun to get rid of human eye defect detection and use machine vision algorithms to help production lines realize defect...

AI Technical Summary

Problems solved by technology

The supervision algorithm based on spectral clustering is to perform spectral clustering on the features extracted from each pixel and divide them into defect features and non-defect features. , this method depends on the quality of the extracted features, and the false alarm rate is high

Images