MATLAB Application for User-Friendly Design of Fully Convolutional Data Description Models for Defect Detection of Industrial Products and Its Concurrent Visualization

In this paper, a fully convolutional data description (FCDD) model is applied to defect detection and its concurrent visualization for industrial products and materials. The authors’ propose a MATLAB application that enables users to efficiently and in a user-friendly way design, train, and test various kinds of neural network (NN) models for defect detection. Models supported by the application include the following original designs: convolutional neural network (CNN), transfer learning-based CNN, NN-based support vector machine (SVM), convolutional autoencoder (CAE), variational autoencoder (VAE), fully convolution network (FCN) (such as U-Net), and YOLO. However, FCDD is not yet supported. This paper includes the software development of the MATLAB R2024b application, which is extended to be able to build FCDD models. In particular, a systematic threshold determination method is proposed to obtain the best performance for defect detection from FCDD models. Also, through three different kinds of defect detection experiments, the usefulness and effectiveness of FCDD models in terms of defect detection and its concurrent visualization are quantitatively and qualitatively evaluated by comparing conventional transfer learning-based CNN models.