基于背景估计的焊缝缺陷检测
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中北大学 生物医学成像与影像大数据山西省重点实验室 太原 030051

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TP391;TG409

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国家自然基金(61801438)、山西省高等学校科技创新项目(2020L0282)、山西省自然科学基金(201901D111161)资助


Weld defect detection based on background estimation
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Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China,Taiyuan 030051,China

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    摘要:

    针对射线检测图像中缺陷识别率低的问题,利用背景估计和差分运算来增强缺陷、抑制复杂背景和噪声。该方法首先利用Otsu分割获得的掩模图像提取焊缝区域;其次通过改进的中值滤波对焊缝区域进行背景估计,反背景差分获得含有缺陷的差分图像;随后根据缺陷与误检边缘处梯度方向的差异性,利用多方向多级梯度有效解决背景残余问题;最后通过自适应阈值分割将含有缺陷的差分图像二值化。实验结果表明,该方法具有较高的缺陷识别率,召回率和准确率分别达91.90%和 90.95%,在实际中具有较好的应用价值。

    Abstract:

    Aiming at the problem of low defect recognition rate in radiographic images, background estimation and differential operations are used to enhance defects and suppress complex background and noise. The method first used the mask image obtained by Otsu segmentation to extract the weld area; Secondly, the background estimation of the weld area was performed by the improved median filter, and the difference image containing the defect was obtained by inverting the background difference; Then, according to the difference of the gradient direction at the edge of the defect and the false detection, the multi-directional and multi-level gradient was used to effectively solve the background residual problem; Finally, t the differential image containing the defect was binarized by adaptive threshold segmentation. After experimental simulation, this method has a high defect recognition rate, with a recall rate and an accuracy rate of 91.90% and 90.95%, respectively, which has good application value in practice.

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张小琳,刘祎,白贇沨,刘研,李文强,桂志国.基于背景估计的焊缝缺陷检测[J].电子测量技术,2022,45(14):116-122

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  • 在线发布日期: 2024-04-08
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