  Similar and indeterminate defect detection of solar cell surface with
heterogeneous texture and complex background is a challenge of solar cell
manufacturing. The traditional manufacturing process relies on human eye
detection which requires a large number of workers without a stable and good
detection effect. In order to solve the problem, a visual defect detection
method based on multi-spectral deep convolutional neural network (CNN) is
designed in this paper. Firstly, a selected CNN model is established. By
adjusting the depth and width of the model, the influence of model depth and
kernel size on the recognition result is evaluated. The optimal CNN model
structure is selected. Secondly, the light spectrum features of solar cell
color image are analyzed. It is found that a variety of defects exhibited
different distinguishable characteristics in different spectral bands. Thus, a
multi-spectral CNN model is constructed to enhance the discrimination ability
of the model to distinguish between complex texture background features and
defect features. Finally, some experimental results and K-fold cross validation
show that the multi-spectral deep CNN model can effectively detect the solar
cell surface defects with higher accuracy and greater adaptability. The
accuracy of defect recognition reaches 94.30%. Applying such an algorithm can
increase the efficiency of solar cell manufacturing and make the manufacturing
process smarter.
