| 李维创,尹柏强.工业金属板带材表面缺陷自动视觉检测研究进展[J].电子测量与仪器学报,2021,35(6):1-16 |
| 工业金属板带材表面缺陷自动视觉检测研究进展 |
| Research progress of automated visual surface defectdetection for industrial metal planar materials |
| |
| DOI： |
| 中文关键词: 自动视觉检测 图像检测 表面缺陷检测 金属板带材 |
| 英文关键词:automated visual inspection (AVI) image detection surface defect detection metal planar materials |
| 基金项目:国家自然科学基金（61971175）、国家重点研发计划“重大科学仪器设备开发”项目（2016YFF0102200）、国家自然科学基金重点项目（51637004）资助 |
|
|
|
|
| 摘要点击次数: 996 |
| 全文下载次数: 11 |
| 中文摘要: |
| 基于计算机视觉的金属板带材表面缺陷检测是冶金工业领域的研究热点,金属板带材制造行业对其表面质量的高标准
要求自动化视觉检测系统及其算法性能不断提升。 通过回顾关于钢板钢带、铝板铝带和铜板铜带等典型金属板带材产品的
110 余篇文献,对基于二维和三维机器视觉的表面检测技术进行了系统综述。 根据算法性质和图像特征,将现有二维缺陷检测
技术分为基于统计、谱、模型和机器学习的 4 类方法,根据三维数据获取方式,将三维缺陷检测技术分为立体视觉测量、激光扫
描仪测量法和结构光测量方法。 对经典算法和新近方法进行了介绍、分析和比较。 最后,对缺陷视觉检测仍存在的挑战和未来
研究趋势进行了讨论与展望。 |
| 英文摘要: |
| Computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical
industry. The high standard of planar surface quality in metal manufacturing industry requires that the performance of automated visual
inspection system and its algorithms is constantly improved. This paper attempts to present a comprehensive survey on surface defect
detection technologies based on two-dimensional and three-dimensional machine vision by reviewing over 110 publications for some
typical metal planar materials products of steel-, aluminium-, copper-plate and strips. According to the nature of algorithms as well as
image features, the existing 2-D methodologies are categorized into four groups: statistical, spectral, model-based and machine learning.
The 3-D defect detection technologies are divided into photometric stereo, laser scanner and structured light measurement methods on the
basis of the way of 3-D data acquisition. These classical algorithms and emerging methods are introduced, analyzed and compared in this
review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted in an abstract
level. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
