摘要
针对焊缝的线形和圆形两种主要缺陷,提出了一种基于支持向量机的焊缝缺陷种类识别算法。首先,对焊缝X射线图像运用模糊C均值聚类、区域填充、均值滤波、边缘检测、大津阈值及谷发现图像预处理算法,获取焊缝缺陷的位置,然后通过逆表面阈值算法将缺陷从焊缝中分割出来;利用基于分段分形纹理分析算法提取焊缝缺陷的特征值;最后将特征值输入到基于支持向量机的焊缝缺陷分类器中,识别出焊缝缺陷种类。试验结果表明,通过对150张焊缝X射线图像进行训练,对80张焊缝X射线图像进行测试,平均正确识别焊缝缺陷种类的准确率达97.5%,满足工业要求。
For linear and circular two kinds of weld defects, proposed a method of weld defect type recognition algorithms based on Support Vector Machine （SVM）. First of all, some image pre-processing algorithms such as fuzzy C means clustering, region filling algorithm, average filtering, edge detection, Otsu thresholding and inverse thresholding, to get the approximate location of weld defects. The information of the particular region will be extracted using segmentation based fractal texture analysis, SVM is used to classify the segmented defect as line or circular defects based on the extracted features lastly. The results showed that, the average accuracy rate is 97.5% for correcting identification of the type of weld defects ,by 150 weld X-ray image is trained and 80 X-ray image weld test, which can meet industrial requirements.
出处
《现代制造工程》
CSCD
北大核心
2017年第10期106-109,共4页
Modern Manufacturing Engineering
基金
广西高等学校优秀中青年骨干教师培养工程资助项目
关键词
焊缝
缺陷
图像处理
支持向量机
weld
defect
image processing
Support Vector Machine （SVM）
