# Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network

## TL;DR

This paper introduces a multi-spectral deep convolutional neural network for solar cell surface defect detection, significantly improving accuracy and efficiency over traditional human-based inspection methods.

## Contribution

The paper develops a multi-spectral CNN model tailored for solar cell defect detection, optimizing model structure and spectral feature analysis for enhanced discrimination.

## Key findings

- Detection accuracy reaches 94.30%.
- Multi-spectral approach improves defect recognition.
- Model demonstrates higher adaptability and efficiency.

## Abstract

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.

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Source: https://tomesphere.com/paper/1812.06220