Assessment of convolutional neural network pre-trained models for detection and orientation of cracks

HIGHLIGHTS

• who: Waqas Qayyum et al. from the Department of Civil Engineering, University of Engineering and Technology, Taxila, Department of Civil Engineering, University of Memphis, Memphis, TN, USA have published the paper: Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks, in the Journal: Materials 2023, 2023, 16, 16, 826 x FOR PEER REVIEW of /2023/
• what: This study assesses seven pre-trained neural networks including GoogLeNet MobileNet-V2 Inception-V3 ResNet18 ResNet50 ResNet101 and ShuffleNet for crack detection and categorization. The aim was to combine CCTV video with the classification . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.