| 基于双目视觉的缺陷深度测量方法 |
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| 引用本文： | 迟大钊,李孙珏,孙昌立,刚铁. 基于双目视觉的缺陷深度测量方法[J]. 焊接学报, 2016, 37(11): 7-10 |
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| 作者姓名： | 迟大钊 李孙珏 孙昌立 刚铁 |
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| 作者单位： | 哈尔滨工业大学 先进焊接与连接国家重点实验室, 哈尔滨 150001 |
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| 基金项目： | 国家自然科学基金资助项目(51375002;51005056),黑龙江省博士后科研启动基金资助项目(LBH-Q13079) |
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| 摘 要： | 准确获取焊缝内部缺陷的深度位置,将为缺陷修补、缺陷危害评价及对焊接工艺分析提供必要的信息.文中阐述基于X射线技术的双标记平移法和旋转法两种缺陷定位方法的基本原理;通过对人工缺陷及实际焊缝缺陷深度测量,验证两种方法的检测精度.结果表明,由于基于射线束为平行光束的假设,双标记平移法缺陷深度检测精度低于旋转法.对厚度为8 mm的焊件,采用双标记平移法获得的缺陷深度的最大绝对误差为1.9 mm,平均绝对误差为1.4 mm,平均相对误差为18%;采用旋转法获得的最大误差为0.5 mm,平均误差为0.3 mm,平均相对误差为4%.
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| 关 键 词： | X射线检测 埋深测量 双目视觉 图像处理 |
| 收稿时间： | 2016-06-04 |
Binocular visual based defect buried depth testing method |
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| CHI Dazhao,LI Sunjue,SUN Changli and GANG Tie. Binocular visual based defect buried depth testing method[J]. Transactions of The China Welding Institution, 2016, 37(11): 7-10 |
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| Authors: | CHI Dazhao LI Sunjue SUN Changli GANG Tie |
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| Affiliation: | State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: | In order to evaluate the effect of defects on the weldment, analysis welding process and repair the weldment, it is necessary to get the buried depth of weld internal defects accurately. This paper introduces the principle of two kinds of defect location methods based on X-ray technique, including double labels translation and rotation methods. The accuracy of the two methods is verified through testing the buried depth of artificial defects and weld defects. The experimental results show that the double labels translation method, which based on the parallel beam assumption, lacks in accuracy compared with the rotation method. For the wledment 8 mm in thickness, using the double labels translation method, the maximum absolute error of defect depth obtained is 1.9 mm, and the average absolute error is 1.4 mm, and the average relative error is 18%. Using the rotation method, the maximum error is 0.5mm, and the average error is 0.3 mm, and the average relative error is 4%. |
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| Keywords: | X-ray testing buried depth testing binocular visual image processing |
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