基于支持向量机的J型坡口接头相贯线检测

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.

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

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.