Research on detection agorithm of solar cell component defects based on deep neural network

Aiming at the problem that the cracked cell in the solar cell module eventually causes the whole cell to break and affect the power generation of the whole component, a method for detecting cracked defects of battery components using convolutional neural network network (CNN), is proposed based on the screening and positioning of the photoluminescence (PL) image of the battery component. The basic idea is to obtain the image of the battery component by using the PL detection technology first, then pre-process the image, filter and locate the target area based on the clustering method, and finally use three convolutional neural network models to detect the defect of the battery, and compare the accuracy. A large number of experimental results verify that the above method can accurately detect the cracking defects of solar cell modules.