[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]
点击复制

全钢子午线轮胎X光图像的缺陷检测研究现状

参考文献/References:
[1] 朱诗顺, 戴骏程, 孙燕, 等. 基于激光散斑干涉的轮胎缺陷无损检测[J]. 军事交通学院学报, 2016, 18(9):44-48 ZHU Shishun, DAI Juncheng, SUN Yan, et al. Nondestructive testing of tire defects based on laser speckle interference[J]. Journal of Military Transportation University, 2016, 18(9):44-48
[2] ZHANG Yan, LI Tao, LI Qingling. Defect detection for tire laser shearography image using curvelet transform based edge detector[J]. Optics and laser technology, 2013, 47:64-71.
[3] 原培新, 张晓慧. 数字图像处理在汽车轮胎X射线检测中的应用[J]. CT理论与应用研究, 2007, 16(2):48-51 YUAN Peixin, ZHANG Xiaohui. X-ray detection of tyre with digital image processing[J]. CT theory and applications, 2007, 16(2):48-51
[4] 王晓明, 陈军芳. 轮胎内部缺陷微波无损检测方法[J]. 轮胎工业, 2004, 24(7):428-431 WANG Xiaoming, CHEN Junfang. Micro-wave non-destructive inspection for interior defects of tire[J]. Tire industry, 2004, 24(7):428-431
[5] 高瑞. X光轮胎图像缺陷识别软件系统结构的研究[D]. 天津:天津大学, 2007. GAO Rui. The research of software system structure in X-ray tire image flaw recognition[D]. Tianjin:Tianjin University, 2007.
[6] 朱越. 工程子午线轮胎X射线图像检测技术中的若干问题研究[D]. 天津:天津大学, 2010. ZHU Yue. Study on X-ray image inspection technology of engineering radial tire[D]. Tianjin:Tianjin University, 2010.
[7] 陈平, 韩焱, 潘晋孝. 变能量X射线多谱成像方法研究[J]. 光谱学与光谱分析, 2013, 33(5):1383-1387 CHEN Ping, HAN Yan, PAN Jinxiao. Research on X-ray multispectrum imaging based on variable energy[J]. Spectroscopy and spectral analysis, 2013, 33(5):1383-1387
[8] 郭奇. 基于X射线轮胎缺陷检测系统设计[D]. 太原:中北大学, 2015. GUO Qi. The design of the tires defect detection system based on X-ray[D]. Taiyuan:North University of China, 2015.
[9] 崔雪红, 刘云, 王传旭, 等. 基于卷积神经网络的轮胎缺陷X光图像分类[J]. 电子测量技术, 2017, 40(5):168-173 CUI Xuehong, LIU Yun, WANG Chuanxu, et al. Defect classification for tire X-ray images using convolutional neural network[J]. Electronic measurement technology, 2017, 40(5):168-173
[10] 黄战华, 刘正, 朱猛, 等. 基于统计特征的轮胎纹理缺陷在线检测[J]. 光学技术, 2009, 35(1):60-62, 66 HUANG Zhanhua, LIU Zheng, ZHU Meng, et al. Defects on-line detection of tire textures based on statistical features[J]. Optical technique, 2009, 35(1):60-62, 66
[11] 刘正. 轮胎X光检测与多纹理图像分析技术的研究[D]. 天津:天津大学, 2009. LIU Zheng. Tyre X-ray inspection and multi-textural image analysis technology[D]. Tianjin:Tianjin University, 2009.
[12] 黄战华, 刘正, 罗文斌, 等. 基于频谱滤波的多纹理提取算法[J]. 光电工程, 2008, 35(8):51-55 HUANG Zhanhua, LIU Zheng, LUO Wenbin, et al. Multi-texture extraction method based on frequency spectrum filtering[J]. Opto-electronic engineering, 2008, 35(8):51-55
[13] 周欣. 轮胎子午线故障检测中的图像处理算法研究[D]. 沈阳:东北大学, 2014. ZHOU Xin. Research on the algorithms of image processing in fault diagnosis of the radial tires’ cords[D]. Shenyang:Northeastern University, 2014.
[14] GUO Qiang, WEI Zhenwen. Tire defect detection using image component decomposition[J]. Research journal of applied sciences, engineering and technology, 2012, 4(1):41-44.
[15] 张岩, 李涛, 李庆领. 基于全变分模型的子午线轮胎X射线图像胎侧缺陷自动检测方法[J]. 青岛科技大学学报(自然科学版), 2017, 38(3):106-112 ZHANG Yan, LI Tao, LI Qingling. Automatic radial tire sidewall defect detection in tire X-ray images based on total variation model[J]. Journal of Qingdao University of Science and Technology (Natural Science Edition), 2017, 38(3):106-112
[16] 朱越, 刘文耀, 袁晔, 等. 一种轮胎X射线图像缺陷提取和分割方法[J]. 光电子·激光, 2010, 21(5):758-761 ZHU Yue, LIU Wenyao, YUAN Ye, et al. A defect extraction and segmentation method for radial tire X-ray image[J]. Journal of optoelectronics·laser, 2010, 21(5):758-761
[17] 林佳佳, 吴则举, 刘中冬. 轮胎X射线0号带束层接头检测定位量化算法的研究[J]. 科学技术与工程, 2016, 16(25):121-125, 136 LIN Jiajia, WU Zeju, LIU Zhongdong. One inspection algorithm to the splice of 0# belt[J]. Science technology and engineering, 2016, 16(25):121-125, 136
[18] 陈勤, 王涛, 刘茵. 轮胎透视图的一种整体区域分割方法[J]. 计算机工程与科学, 2007, 29(4):42-44 CHEN Qin, WANG Tao, LIU Yin. An integer region segmentation method of tyre scenograph[J]. Computer engineering & science, 2007, 29(4):42-44
[19] 张茂强. 子午轮胎缺陷检测方法与系统设计研究[D]. 济南:山东大学, 2014. ZHANG Maoqiang. Research on detection method and system design of radial tire’s defects[D]. Jinan:Shandong University, 2014.
[20] 郑筱智. 基于空域与频域结合的轮胎缺陷检测[D]. 济南:山东财经大学, 2016. ZHENG Xiaozhi. Tire defect detection based on integration of spatial and frequency domains[D]. Jinan:Shandong University of Finance and Economics, 2016.
[21] ZHANG Maoqiang, GUO Qiqng, YANG Xingqiang. Tire defect detection on impurities[J]. Computer aided drafting, design and manufacturing, 2014, 24(1):32-35.
[22] 章玲. 基于图像放缩算法的轮胎缺陷检测系统研究与实现[D]. 济南:山东大学, 2015. ZHANG Ling. The research and implementation of tire defection system with image scaling function[D]. Jinan:Shandong University, 2015.
[23] 李杰. 基于轮胎质量检测成像与常见故障识别算法研究[D]. 沈阳:东北大学, 2012. LI Jie. Research on the image of tyres quality test and common fault recognition algorithms[D]. Shenyang:Northeastern University, 2012.
[24] 张小丽. 轮胎缺陷X光检测图像的处理与识别研究[D]. 天津:天津大学, 2007. ZHANG Xiaoli. Processing and recognition of defects in X-ray tire image[D]. Tianjin:Tianjin University, 2007.
[25] 应崎伟. 汽车轮胎瑕疵的计算机视觉识别系统[D]. 杭州:杭州电子科技大学, 2012. YING Qiwei. Computer version system for tire defects[D]. Hangzhou:Hangzhou Dianzi University, 2012.
[26] 于向茹, 丁健配, 李金屏. 轮胎帘线交叉重叠缺陷检测[J]. 济南大学学报(自然科学版), 2017, 31(6):494-498 YU Xiangru, DING Jianpei, LI Jinping. Detection of cross-over cord defect on tire[J]. Journal of University of Jinan (Natural science Edition), 2017, 31(6):494-498
[27] 袁晔. X光轮胎缺陷自动检测系统的研究[D]. 天津:天津大学, 2008. YUAN Ye. X-ray tire defects automatic detection system[D]. Tianjin:Tianjin University, 2008.
[28] 邵明红. 轮胎缺陷检测的处理和算法研究[D]. 济南:山东大学, 2012. SHAO Minghong. Treatment and algorithm research of tires defects detection[D]. Jinan:Shangdong University, 2012.
[29] 王冰. 工程轮胎X光检测机控制系统的研究与设计[D]. 天津:天津大学, 2010. WANG Bing. Research and design on X-ray otr tire detector control system[D]. Tianjin:Tianjin University, 2010.
[30] 林丽红. 轮胎X光检测机的图像处理算法研究[D]. 广州:华南理工大学, 2016. LIN Lihong. Research on the algorithm of image processing for tire X-ray detector[D]. Guangzhou:South China University of Technology, 2016.
[31] 张岩. 基于计算机视觉的轮胎缺陷无损检测关键问题研究[D]. 青岛:青岛科技大学, 2014. ZHANG Yan. Research on nondestructive tire defect detection using computer vision methods[D]. Qingdao:Qingdao University of Science and Technology, 2014.
[32] 刘宏贵. 轮胎X射线图像缺陷检测算法研究[J]. 传感器世界, 2014, 20(11):14-18 LIU Honggui. Study of defect detection algorithms for X-ray images of tires[J]. Sensor world, 2014, 20(11):14-18
[33] 高鹏. 基于X光图像的轮胎内部缺陷检测技术研究[D]. 天津:天津大学, 2009. GAO Peng. Technology of tire internal defects detection based on the X-ray image[D]. Tianjin:Tianjin University, 2009.
[34] 张潘杰, 郑修楠, 李金屏. 基于穿线法的轮胎帘线断裂缺陷检测[J]. 济南大学学报(自然科学版), 2018, 32(2):102-106 ZHANG Panjie, ZHENG Xiunan, LI Jinping. Tire cord breakage detection based on threading method[J]. Journal of University of Jinan (Natural science Edition), 2018, 32(2):102-106
[35] ZHANG Yan, LEFEBVRE D, LI Qingling. Automatic detection of defects in tire radiographic images[J]. IEEE transactions on automation science and engineering, 2017, 14(3):1378-1386.
[36] GUO Qiang, ZHANG Caiming, LIU Hui, et al. Defect detection in tire X-ray images using weighted texture dissimilarity[J]. Journal of sensors, 2016, 2016:4140175.
[37] 陶沈明. 轮胎缺陷检测系统的研究与实现[D]. 济南:山东大学, 2016. TAO Shenming. The research and implementation of the tire defects detection system[D]. Jinan:Shandong University, 2016.
[38] XIANG Yuanyuan. Tire defect detection using local and global features[J]. Computer aided drafting, design and manufacturing, 2013, 23(4):49-52.
[39] 向媛媛. 基于图像字典表示的缺陷检测算法[D]. 济南:山东财经大学, 2015. XIANG Yuanyuan. Defect detection algorithm based on image dictionary representation[D]. Jinan:Shandong University of Finance and Economics, 2015.
[40] XIANG Yuanyuan, ZHANG Caiming, GUO Qiang. A dictionary-based method for tire defect detection[C]//Proceedings of 2014 IEEE International Conference on Information and Automation. Hailar, China, 2014:519-523.
[41] ZHANG Yan, LI Tao, LI Qingling. Detection of foreign bodies and bubble defects in tire radiography images based on total variation and edge detection[J]. Chinese physics letters, 2013, 30(8):084205.
[42] 张斌, 林森, 高书征. 基于图像处理的轮胎X光图像杂质检测技术[J]. 橡塑技术与装备(橡胶), 2016, 42(9):50-54 ZHANG Bin, LIN Sen, GAO Shuzheng. Tire X-ray image impurity detection based on image processing technology[J]. China rubber/plastics technology and equipment (rubber), 2016, 42(9):50-54
[43] 徐啟蕾. 轮胎X光图像自动识别系统算法研究[D]. 青岛:青岛科技大学, 2006. XU Qilei. Research of automatic tire X-ray image analysis algorithm[D]. Qingdao:Qingdao University of Science and Technology, 2006.
[44] MAK K L, PENG Pai, YIU K F C. Fabric defect detection using multi-level tuned-matched Gabor filters[J]. Journal of industrial and management optimization, 2012, 8(2):325-341.
[45] KUMAR A, PANG G K H. Defect detection in textured materials using Gabor filters[J]. IEEE transactions on industry applications, 2002, 38(2):425-440.
[46] 王泽华, 杨清雷. 轮胎纹理分层的Gabor滤波器设计[J]. 青岛科技大学学报(自然科学版), 2009, 30(6):536-540 WANG Zehua, YANG Qinglei. An algorithm of designing Gabor filters for getting every texture layer of multi-layers texture tyre image[J]. Journal of Qingdao University of Science and Technology (Natural Science Edition), 2009, 30(6):536-540
[47] 林佳佳. 基于轮胎X光图像的0号带束层缺陷检测算法研究[D]. 青岛:青岛科技大学, 2017. LIN Jiajia. Research on defect detection algorithm of #0 belt in tire X-ray images[D]. Qingdao:Qingdao University of Science and Technology, 2017.
[48] 张传海. 纹理无关的裂纹缺陷检测算法[D]. 济南:山东大学, 2013. ZHANG Chuanhai. Texture-invariant detection method for tire crack[D]. Jinan:Shandong University, 2013.
[49] LIU Q, WANG G, GUO Q. Tire Defect Detection Based on Radon Transform[J]. Journal of Computational Information Systems, 2015, 11(21):7841-7848.
[50] 韩延彬, 王杰, 夏英杰, 等. 灰度累积投影直方图在胎冠缺陷检测中的应用[J]. 计算机应用, 2014, 34(8):2221-2226 HAN Yanbin, WANG Jie, XIA Yingjie, et al. Application of gray cumulative projection histogram in detection of tire crown crack[J]. Journal of computer applications, 2014, 34(8):2221-2226

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
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134