基于稀疏求解的输油管道焊缝图像缺陷检测算法研究

Research on Defect Detection Algorithm of Oil Pipeline Weld Image Based on Sparse Solution

提出一种基于稀疏求解的输油管道焊缝图像缺陷检测算法,通过求解稀疏系数完成识别过程,避免了图像的分割与特征值的计算。将现场提取的缺陷和噪声图像作为样本,根据压缩感知理论,首先通过学习从样本中获得字典,利用相关性最小的原则确定字典矩阵数量,然后构建稀疏模型并采用正交匹配追踪算法求解该模型,最后通过求解得出的系数组合判断图像类型。实验表明:该方法能够实现图像缺陷的准确识别。

A sparse solution-based image defect detection algorithm for oil pipeline welds is proposed.The recognition process is completed by solving the sparse coefficient,which avoids image segmentation and feature value calculation.Taking the defect and noise images extracted from the scene as samples,ac-cording to the compressed sensing theory,first obtain dictionaries from the samples through learning,use the principle of least correlation to determine the number of dictionary matrices,then construct a sparse model and use an orthogonal matching pursuit algorithm to solve the model,And finally judge the image type through the coefficient combination obtained by the solution.