In order to improve the accuracy of detection the image defect, a method to detect the edge defect based on modal symmetry algorithm was put forward. The improved PCNN was used to deal with the salt-pepper noise and Gaussian noise in image. On this basis, the semantic learning and annotation of image features were achieved. At first, the corresponding features were extracted from the original image. And then, the semantics were learned by combining the extracted features and the manually labeled library. Combined with the semantic annotation of image, the modal symmetry algorithm was adopted to linearly subtract the data collected by two centrosymmetric sampling points and thus to get the mean value. The asymmetric modal information of the whole image was obtained. Thus, the asymmetric modal could be extracted from the symmetrical modal. Due to the high amplitude of asymmetrical modal signal in defect location. Finally, the defect identification for various locations in image was completed by judging whether the amplitude of asymmetrical modal at the defect location had a sudden change. Following conclusions can be drawn from experimental results. The proposed method has excellent performance in image processing. Meanwhile, this method has high detection accuracy and practicability.

为了提高图像缺陷的检测精度，提出了一种基于模态对称算法的边缘缺陷检测方法。改进的PCNN用于处理图像中的椒盐噪声和高斯噪声。在此基础上，实现了图像特征的语义学习和标注。首先，从原始图像中提取相应的特征。然后，通过组合提取的特征和手动标记的库来学习语义。结合图像的语义标注，采用模态对称算法对两个中心对称采样点采集的数据进行线性相减，得到平均值。获得了整个图像的不对称模态信息。因此，可以从对称模态中提取非对称模态。由于缺陷位置中非对称模态信号的幅度较大。最后，通过判断缺陷位置处的不对称模态的振幅是否突然变化，完成了图像中各个位置的缺陷识别。从实验结果可以得出以下结论。所提出的方法在图像处理中具有优异的性能。同时，该方法具有较高的检测精度和实用性。