焊缝超声相控阵检测数据深度学习降噪方法

Noise reduction method for weld PAUT detection data based on deep learning

超声相控阵检测技术在焊缝检测中具有广泛的应用。超声相控阵检测技术检测信号中常混入噪声导致检测成像时难以分辨真实的缺陷特征。这些噪声主要为无关的反射信号和局部相关的结构噪声,传统的超声图像降噪方法难以有效滤除这些噪声,且存在计算效率低、参数优化复杂等问题。该文提出了一种基于深度学习的焊缝超声相控阵检测技术检测S扫图像的降噪方法,通过搭建深度神经网络降噪模型去除S扫图像中的噪声。经过实验验证,该方法较传统的降噪方法能更有效去除焊缝超声相控阵检测技术检测S扫图中的噪声,保留缺陷的图像细节,并且提高了计算效率,同时避免了人工对不同噪声水平的S扫图像进行参数优化。

Ultrasonic phased-array inspection technology(PAUT)has a wide range of applications in weld inspection,and the PAUT inspection signal is often mixed with noise,making it difficult to distinguish the true defect characteristics during inspection imaging.These noises are mainly irrelevant reflection signals and locally relevant structural noise,which are difficult to be effectively filtered out by traditional ultrasonic image noise reduction methods and have problems such as low computational efficiency and complex parameter optimization.In this paper,we propose a deep learning-based noise reduction method for S-sweep images of weld PAUT inspection,and remove the noise in S-sweep images by building a deep neural network noise reduction model.After experimental verification,the method can remove the noise in the S-sweep image of weld PAUT detection more effectively than the traditional noise reduction method,retain the image details of defects,and improve the computational efficiency,while avoiding the manual parameter optimization of S-sweep images with different noise levels.