摘要
随着工业4.0:智能制造理念的深入,推动工业生产制造进一步由自动化向智能化升级势不可挡。作为制造业的支柱性产业,实现钢铁行业的表面缺陷自主检测具有重要意义。针对现有带钢表面缺陷检测过程中识别率低,无法自主检测等问题,本文提出一种基于CNN的带钢表面缺陷检测算法,引入深度学习知识,通过建立CNN模型,制作数据集,实现了对带钢表面缺陷的自动提取与检测。通过实验验证了该算法的有效性,实验结果表明该算法的准确率在99.99%以上,实验过程中未出现误差,能够满足工业生产方面的要求。
With the deepening of the industry 4.0:intelligent manufacturing concept,promoting industrial production and manufacturing further from automation to intelligent upgrade is unstoppable.As a pillar industry of the manufacturing industry,it is of great significance to realize the self-detection of surface defects in the steel industry.Aiming at the problems of low recognition rate and inability to detect independently in the process of existing strip steel surface defect detection,this paper proposes a CNN-based strip steel surface defect detection algorithm,by introducing deep learning knowledge,establishing CNN model and making data sets,the automatic extraction and detection of strip steel surface defects is realized.The effectiveness of the algorithm is verified by experiments.The experimental result shows that the accuracy of the algorithm is above 99.99%,which is basically error-free and can meet the requirements of industrial production.
作者
杨延西
赵梦
YANG Yan-xi;ZHAO Meng(School of Automation and Information Engineering,Xi'an University of Technology,Xi'an 710048,China)
出处
《重型机械》
2019年第2期25-29,共5页
Heavy Machinery
