常规波纹度测量方法存在检测效率低、检测精度不高等问题，为了实现金属板材表面波纹度的快速准确检测，提出了一种非接触式的表面波纹度无损检测方法，该方法采用改进的中值滤波实现高效快速的图像去噪，使用霍夫变换实现图像的自动旋转，提取了表面图像基于灰度共生矩阵的纹理特征参数，并进行相关性分析。结果表明，对比度、能量、相关和熵等特征参数与波纹度之间存在较好的一致性。以对比度、能量、相关和熵等为输入参数，构建了基于BP神经网络的表面波纹度检测模型，试验验证该方法能够实现金属板材表面波纹度的高效、高精度测量，检测误差仅为4.90%。

The conventional waviness measurement method has the disadvantages of low efficiency and low measurement accuracy. In order to achieve accurate and rapid detection of the surface waviness of a metal sheet, a noncontact nondestructive method for surface waviness detection is proposed. This method uses improved median filtering to achieve efficient and fast image denoising, and in addition it uses Hough transform to realize the image. The automatic rotation of the image extraction method extracts the texture feature parameters of the surface image based on the gray level cooccurrence matrix and performs correlation analysis. The results show that there is a good agreement between the waviness and characteristic parameters of contrast, energy, correlation and entropy. Using contrast, energy, correlation and entropy as input parameters, a surface waviness detection model based on BP neural network was constructed. Experiments verify that the method can achieve high-efficiency and high-precision measurement of the surface waviness of metal plates with a detection error of only 4. 90%.