基于旋转不变HOG特征的焊缝缺陷类型识别算法
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Identification of Weld Defects Based on Rotation-Invariant HOG Feature
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    摘要:

    根据某钢管厂实际采集到的X射线焊缝图像,并通过对焊缝缺陷多样性和形态多变性特点的研究,给出一种基于旋转不变HOG特征提取的焊缝缺陷类型识别算法. 首先,将项目前期已经检测到的多种缺陷进行分类和统计,截取每幅焊缝图像的ROI部分,构成实验所需的缺陷样本. 通过尺度变换和圆形细胞划分方式,得到具有尺度不变性和旋转不变性的HOG特征,将所有样本特征进行PCA降维,维数由贡献度决定. 最后使用LSSVM模型对缺陷进行类型识别. 通过研究block块重叠范围对识别正确率的影响,发现在一定范围内,重叠范围越大,识别正确率越高. 该算法通过改进传统HOG特征提取方式,提高了缺陷识别的正确率.

    Abstract:

    According to the X-ray weld image collected by a steel pipe factory and the study on diversity and morphological variability of weld defects, a weld defect identification algorithm based on rotation invariant HOG feature extraction is proposed. First of all, we classify different types of defects detected to extract ROI of each image, all of which constitute the defect samples required by the experiment. By means of scale transformation and circular cell division, we obtain HOG characteristics with scale invariance and rotation invariance. Then all the sample features are reduced by PCA dimensionality reduction. The dimension is determined by the contribution. Finally, the LSSVM model is used to identify the defects. By studying the effect of block overlap on the recognition accuracy rate, it is found that the higher overlap range, the higher correctness in a certain unit. The algorithm improves the accuracy of defect recognition by improving the traditional HOG feature extraction method.

    参考文献
    [1] 杨川. 焊缝缺陷图像特征提取研究[硕士学位论文]. 武汉: 武汉理工大学, 2010.
    [2] 罗来齐. 管道缺陷检测与识别方法研究与实现[硕士学位论文]. 镇江: 江苏大学, 2016.
    [3] Cui WC, Chen S, Yu TS, et al. Feature extraction of X-ray chest image based on KPCA. Proceedings of the 2nd International Conference on Computer Science and Network Technology (ICCSNT). Changchun, China. 2012. 1263-1266.
    [4] Daum W, Rose P, Heidt H, et al. Automatic recognition of weld defects in X-ray inspection. British Journal of Nondestructive Testing, 1987, 29(2): 79-81.
    [5] 罗滨, 朱长仁, 任云, 等. 基于主方向的旋转不变HOG特征. 现代电子技术, 2015, 38(22): 84-87, 90.
    [6] 汤彪, 左峥嵘, 李明. 基于旋转不变HOG特征的图像匹配算法. 中国科技论文在线, http://www.paper.edu.cn/release-paper/content/201301-1025.
    [7] Ashour MW, Khalid F, Halin AA, et al. Machining process classification using PCA reduced histogram features and the support vector machine. Proceedings of 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). Kuala Lumpur, Malaysia. 2015. 414-418.
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王璐,王新房.基于旋转不变HOG特征的焊缝缺陷类型识别算法.计算机系统应用,2018,27(2):157-162

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  • 收稿日期:2017-05-17
  • 最后修改日期:2017-06-08
  • 在线发布日期: 2018-02-05
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