基于自适应SVM决策树的焊缝缺陷类型识别

李 坤; 文 斌; 任清安; 罗爱民

基于自适应SVM决策树的焊缝缺陷类型识别

1.
四川大学电子信息学院, 成都 610064
2.
成都信息工程学院, 成都 610225
3.
四川大学数学学院, 成都 610065
4.
四川大学轻纺与食品学院, 成都 610065

基金项目:

成都信息工程学院自然科学与技术发展基金资助(csrf200805)

详细信息

作者简介:
李坤(1987-), 男, 在读本科, 主要研究方向是光信息科学与技术。

中图分类号: TG115.28
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出版历程
- 收稿日期: 2009-04-02
- 刊出日期: 2010-03-09

Welding Defects Classification Based on Adaptive SVM Decision Tree

摘要

摘要: 针对传统X射线焊缝缺陷检测方法普遍存在分类识别精度不高的问题, 提出了一种基于分离程度的自适应SVM决策树算法。首先对滤波后的X-Ray焊缝缺陷图像进行数学形态学重建, 然后根据分离程度, 每次将分离程度最大的缺陷类别首先分离出来, 构造自适应二叉树的SVM分类器, 从而达到了减小二叉树的累积误差, 得到了分类性能优良的的SVM决策树, 并用其对X-Ray焊缝缺陷图像进行分类识别。实验结果表明, 该算法取得了好的分类精度和识别效果。
- 决策二叉树 /
- 支持向量机 /
- 分离程度 /
- 数学形态学 /
- 缺陷识别
Abstract: An adaptive SVM(Support Vector Machines) based on binary tree using the degree of separation is proposed in this paper, aiming at the problem that it’s difficult for traditional detection methods to accurately identify the welding defects of X-Ray images. Firstly, mathematical morphological reconstruction is applied to the filtered X-Ray images of welding defects. It is proposed to separate category of defects with the largest degree of separation as a priority, and to construct adaptive SVM classifiers based on binary tree, thus decreasing the accumulated error. Finally, a SVM decision tree of good classification performance can be obtained, which is used to classify and identify the X-Ray images of welding defects, and it shows that the algorithm has made a good classification and recognition accuracy results.
- Binary decision tree /
- Support vector machine /
- Degree of separation /
- Mathematical morphology /
- Welding defects classification

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参考文献(16)

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基于自适应SVM决策树的焊缝缺陷类型识别

作者简介: 李 坤(1987-), 男, 在读本科, 主要研究方向是光信息科学与技术。

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出版历程

Welding Defects Classification Based on Adaptive SVM Decision Tree

计量

出版历程

目录

作者简介:
李坤(1987-), 男, 在读本科, 主要研究方向是光信息科学与技术。