摘要
针对焊缝射线检测图像中缺陷类型识别准确度较低的问题,提出了一种基于直接多类支持向量机的缺陷类型识别方法.该方法将焊缝缺陷类型识别问题转化为一个约束优化问题,采用由缺陷边缘特征和区域特征构成的特征向量对缺陷进行描述,解决了在实际训练样本较少的情况下,提高缺陷类型识别准确度的问题.实验表明,该方法的识别准确度为94.25%,比一对一支持向量机和多层感知神经网络的高,并且通过引入区域特征提高了特征组的缺陷描述能力.
To improve the recognition accuracy of weld defects in the radiographic image,a method based on direct multiclass support vector machine （SVM） is proposed to recognize the defect types,where the recognition of weld defects is regarded as a constrained optimization problem,and the edge-based features and region-based features of the weld defect are employed as the feature vector. This method solves the difficulty of achieving higher accuracy under a small training set. The experimental results demonstrate that the recognition accuracy of the method gets 94.25%,higher than that of the one-versus-one SVM and multi-layer perceptron （MLP） neural network,and the introduced region-based features improve the characterization capability of the feature group.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2010年第7期100-103,共4页
Journal of Xi'an Jiaotong University
关键词
射线检测图像
缺陷类型识别
直接多类支持向量机
radiographic image
recognition of weld defect
direct multiclass support vector machine
