Edge Defect Detection of Network Image by the Application of Modal Symmetry

Abstract

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.