Real-time Steel Surface Defect Recognition Based on CNN

Abstract

Steel is one of the most important building materials of our time, and the process of producing flat plates is complex. Before steel is shipped or delivered, the sheets must undergo a thorough inspection procedure to avoid defects. Identification and classification of rolled metal surface defects are one of the main tasks for the correct evaluation of product quality. This work aims to develop a method for recognizing and classifying defects of metal surfaces by their images in real-time. The algorithm is aimed at improving production standards and process efficiency. In this paper, Deep Learning (DL) and Computer Vision (CV) techniques are used to solve the problem of defect detection on the steel surface sheets. Convolutional Neural Network (CNN) architectures are compared, and various steel defects are detected and recognized. The result of this work is a comparative analysis of DL models and the choice of an algorithm designed to search and classify defects in real-time. The use of one CNN model can make it possible to create a tool that greatly facilitates the work of a person.