Detecting and Classifying Surface Defects in Rolled Steel Sheets Using Deep Learning Techniques

The paper focuses on using Deep Learning (DL) and Computer Vision (CV) techniques to detect surface defects in rolled metal products. By utilizing a Convolutional Neural Network (CNN), various surface defects can be detected and recognized, ultimately improving production standards and certification of the metal products. Two types of lighting, diffuse and side, are used to improve defect detection, and image preprocessing methods are employed to enhance the quality of the input data. The purpose of this work is to develop a method for recognizing and classifying defects in metal surfaces from their images in real time.