Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms

This research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. This study is interesting since it does a thorough assessment of a wide variety of models and concentrates on high-performance architectures and lightweight models that may be used in contexts with…

• DO
  Deanship of Scientific Research, King Saud University

  Award: RGP2/167/45