基于神经网络的钢材表面缺陷快速检测

表面缺陷是影响钢材质量的重要因素，钢材表面缺陷图像在线快速检测已成为国内外学者研究的热点课题。研究钢材表面缺陷识别技术不仅具有一定的理论价值，更具有实际的应用前景。本文设计并通过仿真实现了冷轧带钢表面缺陷检测系统及缺陷分类系统，重点研究了BP神经网络方法及图像处理技术在钢材表面缺陷识别中的应用，实现冷轧带钢表面缺陷的快速自动分类。

Rapid Detection of Steel Surface Defects Based on Neural Network

The surface defect is an important factor influencing the quality of steel.The current research of this field focuses on the rapid defect inspection in the steel surface at home and abroad. A study into the technology of surface defect recognition is of great significance both in theory and practice. Inspec- tion and classification system of defects in the surface of cold rolled strip steel is designed. The re- search focuses on the application of BP Neural Network Method and image processing technique to the recogni- tion of steel surface defects, which realizes the rapid auto-classification of defects in tbe surface of cold rolled strip steel.