上海交通大学学报

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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