基于压缩传感技术的埋弧焊X射线焊缝图像缺陷检测

A new algorithm for detecting defects of sub-arc welding xray image based on compress sensor theory

将压缩传感理论引入X射线焊缝图像缺陷判断,提出将判断X射线焊缝图像是否含有缺陷问题作为一个模式识别问题处理,将待检测图像视为样本图像的线性组合,通过求取系数向量来判断图像是否存在缺陷.为实现系数向量的稀疏化,提出利用罚函数的方法求解0范数最小问题的近似最优解,提出新的光滑可导的0-1惩罚项函数,使求0范数最优成为可能.在此基础上分别利用1范数最小和2范数最小求取系数向量,并利用混淆矩阵对所求结果进行分析.结果表明,综合考虑0,1,2范数最小化所得系数的识别算法灵敏度可达99%,特异度可达98%.

An efficient X-ray radiography image analysis algorithm is developed for submerged-arc welding defects detection. The compress sensor theory is incorporated into the new algorithm,and the problem of defect detection is changed to a model recognition problem. The given X-ray image is represented by a linear combination of few model X-ray images. If a dictionary of model defect images and noise images are obtained,the coefficient vector can give important information for deciding the given image is defect or noise. Thus a sparse vector representation is sought by performing l0,l1 and l2 norm minimization. Finally,the sparse representations of the defect part and noisy part are compared in the context of a maximum likelihood ratio test which leads to the final classification. Tested with 800 x-ray radiography images obtained from a factory production line,the proposed algorithm achieves a sensitivity 99% and specificity 98%.