[1]逄增治,郑修楠,李金屏.全钢子午线轮胎X光图像的缺陷检测研究现状[J].智能系统学报,2019,14(4):793-803.[doi:10.11992/tis.201806014]
PANG Zengzhi,ZHENG Xiunan,LI Jinping.Research status of defect detection in X-ray images of all-steel radial tires[J].CAAI Transactions on Intelligent Systems,2019,14(4):793-803.[doi:10.11992/tis.201806014]
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PANG Zengzhi,ZHENG Xiunan,LI Jinping.Research status of defect detection in X-ray images of all-steel radial tires[J].CAAI Transactions on Intelligent Systems,2019,14(4):793-803.[doi:10.11992/tis.201806014]
全钢子午线轮胎X光图像的缺陷检测研究现状
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第4期
页码:
793-803
栏目:
综述
出版日期:
2019-07-02
- Title:
- Research status of defect detection in X-ray images of all-steel radial tires
- Keywords:
- all-steel radial tires; X-ray images; defect detection; image processing; machine learning; dictionary learning; Fourier transformation; Gabor transformation
- 分类号:
- TP391
- 摘要:
- 全钢子午线轮胎结构复杂，生产过程中会出现多种缺陷，利用图像处理技术能够对全钢子午线轮胎的X光图像进行缺陷检测。为了更清楚地梳理现存算法，对当前全钢子午线轮胎X光图像的缺陷检测算法做了大量调研。首先，对全钢子午线轮胎缺陷检测的研究现状及发展历程做了梳理和回顾；然后，对全钢子午线轮胎缺陷进行分类，根据不同类型的缺陷分别介绍该类缺陷的主要检测方法，并对检测方法进行优缺点分析；最后，指出未来在全钢子午线轮胎缺陷研究领域中面临的挑战，展望了轮胎缺陷检测技术的发展方向。
- Abstract:
- All-steel radial tires have complex structures and there may be many defects in their production process. Image processing technology can be used to detect the defects of all-steel radial tires in X-ray images. In order to better sort out the existing algorithms, we have conducted research into the current defect detection algorithms for X-ray images of all-steel radial tires. First, we studied the current status and development history of defect detection for all-steel radial tires. We then classified the defects of all-steel radial tires, and made an introduction to the main detection methods for these defects according to different defect types, and analyzed their advantages and disadvantages. Finally, we pointed out the challenges in future research and look forward to the development direction of defect detection technology.
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备注/Memo
收稿日期:2018-06-06。
基金项目:国家自然科学基金项目（61701192）；山东省重点研发计划项目（2017CXGC0810）；山东省科技重大专项（新兴产业）项目（2015ZDXX0801A03）；山东省教育科学规划“教育招生考试科学研究专设课题”（ZK1337212B008）.
作者简介:逄增治,男,1995年生,硕士研究生,主要研究方向为图像处理与模式识别;郑修楠,女,1993年生,硕士研究生,主要研究方向为图像处理与模式识别;李金屏,男,1968年生,教授,博士,主要研究方向为机器视觉、图像处理、模式识别、优化算法。主持和承担国家级、省级科研10余项,企业合作项目10余项。发表学术论文近200篇。
通讯作者:李金屏.E-mail:ise_lijp@ujn.edu.cn
更新日期/Last Update:
2019-08-25