| 基于Fourier拟合曲面的X射线焊缝缺陷检测 |
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| 引用本文： | 李雪琴,刘培勇,殷国富,蒋红海.基于Fourier拟合曲面的X射线焊缝缺陷检测[J].焊接学报,2014,35(10):61-64. |
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| 作者姓名： | 李雪琴 刘培勇 殷国富 蒋红海 |
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| 作者单位： | 1.西华大学机械工程与自动化学院, 成都 610039 |
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| 基金项目： | 国家科技支撑计划课题资助项目(2012BAF06B02);四川省科技支撑计划课题资助项目(2011CGZ0049) |
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| 摘 要： | 针对在强噪声、低对比度及复杂背景特征下X射线焊缝图像的缺陷检测问题,提出了去噪处理、焊缝边缘分割及缺陷检测的方法.用快速离散Curvelet变换和循环平移相结合的方法,对焊缝图像进行滤波去噪,同时对图像列灰度曲线用最大类间方差法提取焊缝区域.在图像预处理后,采用三阶Fourier曲线对图像列灰度曲线进行拟合并扩展到三维空间,构造出自适应阈值面,最后利用原图像与构造曲面三维灰度图的灰度值差异,准确分割背景与缺陷区域.结果表明,与传统缺陷检测算法相比,该方法能准确提取出焊缝缺陷,漏检率和误判率低,准确率可达95%.
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| 关 键 词： | X射线 焊缝图像 缺陷检测 Curvelet变换 Fourier拟合 |
| 收稿时间： | 2013/4/8 0:00:00 |
Weld defect detection by X-ray images method based on Fourier fitting surface |
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| LI Xueqin,LIU Peiyong,YIN Guofu and JIANG Honghai.Weld defect detection by X-ray images method based on Fourier fitting surface[J].Transactions of The China Welding Institution,2014,35(10):61-64. |
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| Authors: | LI Xueqin LIU Peiyong YIN Guofu and JIANG Honghai |
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| Affiliation: | 1.School of Mechanical Engineering and Automation, Xihua University, Chengdu 610039, China2.School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China3.School of Electrical and Mechanical Engineering, Kunming University of Science and Technology, Kunming 650504, China |
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| Abstract: | To solve such problems as the strong noise, low contrast and complex background of X-ray image in the weld defect detection, a method of the noise reduction, weld edge segmentation and defects extraction was proposed. The fast discrete curvelet transform and cycle shift were applied to reduce noise of the weld image, and the Otsu method was utilized to extract the weld region by the column gray curves of the image. Cubic Fourier curve was used to fit the column gray curves after preprocessing of weld image, and the adaptive threshold surface was constructed by extending fitting curves to 3D space. Finally, the background and the defect area were segmented accurately with the gray differences of 3D gray image between the original image and the reconstructed surface. Experiment results show that the method can extract weld defects accurately. Compared with traditional defect detection algorithm, it has the lower undetected rate and fewer misinterpretations, the accuracy rate could reach 95%. |
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| Keywords: | X-ray weld image defect detection curvelet transform Fourier fitting |
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