Defect detection of solar cells based on improved TCNN

Xiaoyu Song; Jiahua Feng; Shihua Sun; Shuai Yuan

doi:10.1117/12.2636820

29 April 2022 Defect detection of solar cells based on improved TCNN

Xiaoyu Song, Jiahua Feng, Shihua Sun, Shuai Yuan

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 1224712 (2022) https://doi.org/10.1117/12.2636820
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

Aiming at the problem that traditional convolutional neural network (CNN) for solar cell defect detection cannot learn complex invariance, a defect detection method based on improved tiled convolutional neural network (TCNN) is proposed. First, the image is preprocessed by morphological smoothing method to remove grid lines and noise in the image. Then, a random forest classifier is used to replace the TCNN output layer to enhance the generalization ability of TCNN. Finally, TCNN is used to learn the complex invariance of defect images for defect detection. In order to avoid TCNN falling into local optimum, differential evolution algorithm (DE) is introduced to optimize TCNN. The experimental results show that the improved TCNN can quickly and accurately detect the surface defects of solar cells, and the current overall recognition rate is as high as 96.8%, which is 2.3% higher than the traditional CNN, which verifies the effectiveness of the proposed method.

Citation Download Citation

Xiaoyu Song, Jiahua Feng, Shihua Sun, and Shuai Yuan "Defect detection of solar cells based on improved TCNN", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 1224712 (29 April 2022); https://doi.org/10.1117/12.2636820

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