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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.