Steel Surface Defect Classification Using Deep Residual Neural Network
Ihor Konovalenko,
Pavlo Maruschak,
Janette Brezinová,
Ján Viňáš,
Jakub Brezina
Affiliations
Ihor Konovalenko
Department of Industrial Automation, Ternopil National Ivan Puluj Technical University, Rus’ka Str. 56, 46001 Ternopil, Ukraine
Pavlo Maruschak
Department of Industrial Automation, Ternopil National Ivan Puluj Technical University, Rus’ka Str. 56, 46001 Ternopil, Ukraine
Janette Brezinová
Department of Engineering Technologies and Materials, Faculty of Mechanical Engineering, Technical University of Košice, Mäsiarska 74, 040 01 Košice, Slovakia
Ján Viňáš
Department of Engineering Technologies and Materials, Faculty of Mechanical Engineering, Technical University of Košice, Mäsiarska 74, 040 01 Košice, Slovakia
Jakub Brezina
Department of Engineering Technologies and Materials, Faculty of Mechanical Engineering, Technical University of Košice, Mäsiarska 74, 040 01 Košice, Slovakia
An automated method for detecting and classifying three classes of surface defects in rolled metal has been developed, which allows for conducting defectoscopy with specified parameters of efficiency and speed. The possibility of using the residual neural networks for classifying defects has been investigated. The classifier based on the ResNet50 neural network is accepted as a basis. The model allows classifying images of flat surfaces with damage of three classes with the general accuracy of 96.91% based on the test data. The use of ResNet50 is shown to provide excellent recognition, high speed, and accuracy, which makes it an effective tool for detecting defects on metal surfaces.
