THE USE OF NEURAL NETWORK TECHNOLOGIES TO SOLVE THE PROBLEM OF CLASSIFICATION OF ROLLED METAL DEFECTS (Modern Problems of Metallurgy, 2023)
The main goal of the work is to create a software system that performs the classification of rolled metal surface defects with high accuracy (over 90%) and high speed (no more than 1 second per 1 image) through the use of artificial neural networks. To achieve the goal, it is necessary to perform the following tasks: 1) perform a study of the subject industry, determine the possibility of using neural networks to solve the problem of classifying rolled metal defects; 2) build a neural network model suitable for solving the problem of classifying visible defects in rolled metal products; 3) generate training, test and validation samples based on the database of hot rolled metal surface defects provided by Northeastern University (NEU); perform augmentation of the generated samples; 4) design and implement a defect classification system; 5) conduct testing and computational experiments. As a result of the work, a software system was developed for the classification of rolled metal defects based on neural network technologies, which is capable of classifying a defect in an image with an accuracy of at least 90% in less than 1 s of time.
Keywords: Neural network; software system; classification; defect; rolled metal; augmentation; API; web interface; REST; MobileNet; InceptionV3; Python; Django web/rest framework; JavaScript; Bootstrap; dataset; dataset.