目的 为检测金属产品表面缺陷提供一种有效的方法，希望可以对金属产品表面质量进行监控。方法 首先，引入自适应中值滤波方法对原始图像中的噪声进行滤除，以提高金属表面缺陷的检测正确度。然后，利用图像梯度的倒数对传统的P-M非线性扩散模型中的扩散因子进行改进，使得金属表面图像中梯度值较大的区域得以平滑，同时保持其他区域的平滑度不变。将金属表面的原始图像与经过非线性扩散后的图像进行图像差分运算，以消除光照度对金属表面图像的影响，获取均匀背景的金属表面图像，使得缺陷区与非缺陷区的对比度得以增强。最后，通过差分图像中图像块的标准差构造自适应二值化模型，对差分图像进行二值化，以提取金属表面的缺陷区域，实现对金属表面缺陷的准确检测。结果 通过对具有划痕、裂纹、缺口以及锈斑缺陷的图像进行检测表明，该方法能够对金属表面缺陷进行准确的检测。结论 所设计的方法能对金属表面缺陷进行检测，并且检测精度也优于当前其他金属表面缺陷检测方法。

The work aims to provide an effective method for detecting surface defects of metal products, so as to monitor surface quality of metal products. Firstly, adaptive median filtering method was introduced to filter noise in original image, so as to improve detection accuracy of metal surface defects. Then, reciprocal of image gradient was used to improve diffusion factor of traditional P-M nonlinear diffusion model, so that such areas with higher gradient value in the metal surface image could be smooth, while smoothness of other areas remained unchanged. The difference between the original image of metal surface and the image after nonlinear diffusion was applied to eliminate the influence of illumination on metal surface image, and obtain the image of metal surface with uniform background, so as to enhance the contrast between defect area and non-defect area. Finally, the adaptive two valued model was constructed based upon standard deviation of image block in the differential image, the differential image was binarized to extract defect area on metal surface and detect metal surface defects accurately. Detection of cracks, scratches, notch and rust defect images showed that this method could be used to accurately detect metal surface defects. The method designed in this paper can detect metal surface defects, and detection accuracy is also superior to other detection methods of metal surface defects.