上海交通大学学报(自然版) ›› 2015, Vol. 49 ›› Issue (03): 310-314.

• 金属学与金属工艺 • 上一篇    下一篇

基于支持向量机的J型坡口接头相贯线检测

庹宇鲲1,胡绳荪1,申俊琦1,陈昌亮2,谷文3,李坚3   

  1. (1. 天津大学 天津市现代链接技术重点实验室, 天津 300072;2. 天津市高速切削与精密加工重点实验室, 天津 300222;3. 中国第一重型机械集团核电石化事业部, 辽宁 大连 116113)
  • 收稿日期:2014-07-03 出版日期:2015-03-30 发布日期:2015-03-30
  • 基金资助:

    国家自然科学基金项目(50975195),天津市应用基础及前沿技术研究计划项目(10JCYBJC06500)资助

Detection of Intersecting Line Dedicated to J-groove Joints Based on SVM

TUO Yukun1,HU Shengsun1,SHEN Junqi1,CHEN Changliang2,GU Wen3,LI Jian3   

  1. (1. Tianjin Key Laboratory of Advanced Joining Technology, Tianjin University, Tianjin 300072, China;2. Tianjin Key Laboratory of High Speed Cutting and Precision Machining, Tianjin 300222, China;3. Nuclear Power and PetroChemical Business Group, China First Heavy Industries, Dalian 116113, Liaoning, China)
  • Received:2014-07-03 Online:2015-03-30 Published:2015-03-30

摘要:

摘要:  针对核电压力容器中J型坡口焊缝的自动化焊接,应用图像处理技术,结合支持向量机(SVM)分类器,研究了核电压力容器封头与圆管相贯线检测算法. 以颜色矩特征和灰度共生矩阵特征组合作为特征向量,利用SVM对图像进行分类,结合滑块机制和投票机制可以生成相贯线区域高亮的二值图像,利用二次曲线对二值图像中最大轮廓进行拟合,获取相贯线的准确位置. 结果表明:算法具有较高的鲁棒性和实时性,SVM分类器准确率达到95.6%,每幅图像处理时间在170 ms以内.

关键词:  , J型坡口, 圆管相贯线, 支持向量机

Abstract:

Abstract: In the automatic welding process of nuclear reactor vessels, the joints of the tubesphere intersections are usually complex. This paper presents a complete algorithm in detecting the intersecting line of the Jgroove based on image processing techniques and the support vector machine (SVM) classifier. Taking the combination of color moment feature and optimized gray level cooccurrence matrix (GLCM) feature as the feature vector, classification was made using the SVM classifier to acquire a binary image with the intersection area highlighted, while the image block sliding mechanism and voting mechanism were applied. The precise position of the intersecting line was detected by fitting a quadratic curve of the maximum outline. Experimental results show that the algorithm can endure a complex environment and can achieve realtime requirement. The accuracy of the SVM classifier is up to 95.6%, and the processing time of each image is less than 170 ms.

Key words: J-groove joints; sphere-tube intersecting line, support vector machine

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