Weld Defect Classification Based on Texture Features and Principal Component Analysis
发布时间：2025-04-30
点击次数：
- 发布时间：
- 2025-04-30
- 论文名称：
- Weld Defect Classification Based on Texture Features and Principal Component Analysis
- 发表刊物：
- Insight
- 摘要：
- In the traditional process used to classify weld defects in radiographic images, the precision of the weld defect region (WDR) segmentation and the validity of the defect feature extraction significantly influence the classification accuracy. To address the abovementioned issues, a feature extraction and weld defect classification method based on texture features and principal component analysis (PCA) is proposed in this paper. Firstly, the weld seam region (WSR) in the radiographic image, which contains several WDRs, is segmented, rather than the individual WDRs. Then, the texture features of the WSR are extracted for use in the weld defect classification process. Since the extracted texture features have high dimensions and are partially redundant with each other, PCA is then used to reduce the dimensions of these features. Finally, a multiclass support vector machine (SVM) is used to classify the defects based on the obtained principal components. The experimental results demonstrate that the proposed method can effectively extract the general features of weld defect types and can achieve a classification accuracy of up to 90.4%. Moreover, the proposed method can classify slag (SL) and porosity (PO) defects more accurately than traditional classification methods.
- 合写作者：
- Hongquan Jiang, Yalin Zhao, Jianmin Gao, Zhao Wang
- 卷号：
- 58(4)
- 页面范围：
- 194-199
- 是否译文：
- 否
- 发表时间：
- 2016-04-15