Research on steel profiles surface defect detection algorithm based on machine vision

Abstract

Steel profiles is widely used in many different fields and is a fundamental raw material in the industrial production process. However, surface defects often occur during the steel production process due to differences in testing environments and production raw materials. Therefore, it is crucial to detect surface defects in steel profiles. However, with the progress of society, science and technology, traditional machine vision inspection methods are increasingly difficult to meet the requirements of modern industrial production in terms of accuracy and real-time performance in the actual inspection process. In this context, this thesis provides an in-depth analysis of deep learning-based defect detection algorithms and improves the state-of-the-art YOLOv5 object detection algorithm according to the characteristics of steel profiles surface defects in order to improve the detection speed and accuracy. First, the steel dataset is regrouped using the K-means algorithm based on IOU measured distances to generate different sets of anchor frames. Second, MixUp was integrated into the Mosaic data extension to reduce overfitting and improve model generalization. Then, the network structure was improved and an attention module was added to further improve the ability to extract network features.