摘要
热轧带钢是钢铁行业的主要原材料之一,其表面质量控制一直是生产过程智能检测的重点任务。针对带钢表面缺陷自动在线检测逐步取代人工检测的现状,概述带钢表面缺陷检测方法,着重阐述基于机器视觉的表面缺陷检测方法,比较分析传统机器视觉、深度学习方法在带钢表面缺陷检测的应用,探讨带钢表面缺陷检测中存在的关键技术问题,并对其未来发展趋势做展望。传统机器视觉的带钢缺陷检测方法检测速度较高,但精度较低;主流深度学习的缺陷检测方法检测精度高,但速度较慢。如何在保证检测实时性的前提下提高算法的准确性和鲁棒性,不仅是自动化和智能化检测的发展趋势,也是基于机器视觉部署在实际工业现场的关键所在。
Hot-rolled strip is one of the main raw materials in the iron and steel industry,and its surface quality control has always been the key task of intelligent detection in the production process.Aiming at the present situation that automatic online detection of strip surface defects gradually replaced manual detection,the detection methods of strip surface defects were summarized,and the machine-vision-based methods were emphatically introduced,and the application effects of traditional machine vision and deep learning methods of strip surface defects detection were compared and analyzed,and the key technical problems and future development trend of strip surface defect detection were discussed and prospected respectively.The traditional machine-vision-based method of strip defects detection has high detection speed but low accuracy.The mainstream defect detection methods of deep learning have high detection accuracy but slow speed.Therefore,how to improve the accuracy and robustness of the algorithm on the premise of ensuring real-time detection is not only the development trend of detection automation and intelligence,but also the key to deploy in the actual industrial site based on machine vision.
作者
米春风
卢琨
汪文艳
王兵
MI Chunfeng;LU Kun;WANG Wenyan;WANG Bing(School of Electrical&Information Engineering,Anhui University of Technology,Maanshan 243032,China;Key Laboratory of Power Electronics and Motion Control Anhui Education Department,Anhui University of Technology,Maanshan 243032,China)
出处
《安徽工业大学学报（自然科学版）》
CAS
2022年第2期180-188,共9页
Journal of Anhui University of Technology（Natural Science）
基金
国家自然科学基金项目(62172004,61672035,61872004)
安徽省高校自然科学基金项目(KJ2019ZD05)。
关键词
热轧带钢
表面缺陷
检测方法
机器视觉
hot-rolled strip
surface defect
detection method
machine vision
