###### Abstract
The low-frequency components were combined with morphological transformation to remove the background, the defect information of the high-frequency components was extracted with the help of filters, and finally the defect contours were reconstructed. [PERSON] et al. [12] proposed a multi-feature fusion detection algorithm for CT surface defects. The improved the scale-invariant feature transform (SIFT), the color moments were combined to extract image features, and the combined integrated feature vectors were sent to the SVM classifier for final classification. As mentioned earlier, traditional image processing methods that rely on artificial design defect features and utilize machine learning classification have made some achievements in defect detection for CTs. Many scholars who directly adopt deep convolutional neural networks (DCNN) for feature extraction and classification have also achieved good defect detection accuracy. [PERSON] et al. [13] used the improved U-Net model to extract the characteristics of magnetic tile defects and trained the neural network for defect classification. The defect detection accuracy rate reached 94%. [PERSON] et al. [14] proposed a detection algorithm that synchronously compares the CT defect image and the normal image, they utilized a deep neural network to detect the defect image and the corresponding normal image at the same time, and judged the detection result through a comparator to reduce the false detection rate. [PERSON] et al. [15] presented a detection method for CT surface defects based on a lightweight convolutional auto-encoding network. The auto-encoder was trained with non-defective CT images, and defects were gained by defective image reconstruction. [PERSON] et al. [1] extracted GLCM features of CT to characterize image defects, CNN was applied to the classification, and the accuracy rate reached 83% on 20 cm tiles. [PERSON] et al. [16] combined traditional image processing methods with deep learning, and traditional methods such as contrast enhancement and equalization were used as preprocessing units to improve the CT cracks and background contrast, and then trained the U-Net segmentation model for defect segmentation. [PERSON] et al. [17]applied the YOLOv3-tiny model to the mobile phone ceramic substrate to detect scratches with an accuracy of 89.9%. We summarized the results of previous researchers, as shown in Table 1.  
Although traditional and deep learning algorithms have accomplished substantial success in identifying and classifying surface defects of CT, they are limited to monochromatic or simple texture CTs. We can conclude that existing algorithms failed to better detect surface defects for CTs with complex textures resulting in low accuracy, and employing the discriminator to recheck the detection results can improve the defect detection accuracy from Table 1. Perhaps the main reason lies in that non-defective CTs with complex textures are more likely to be identified as defects [18]. Meanwhile, obtaining various patterns in advance is unrealistic due to the variation of CT styles, and it belongs to the open-set problem of pattern recognition. This paper proposes a CNFA for improving the detection accuracy of surface defects for CT, which was shown to have stable performances on both the validation and test sets, including uncomplicated and complex textures. It provides a theoretical and practical reference for surface defect detection of products with complex and changeable textures in industrial environments such as steel, fabrics, and leather. The remaining part of this paper proceeds as follows: a detailed description of the proposed methodology is provided in Section 2. We first introduce the structure and flow of the entire algorithm and then explore how the comparator, detector, and discriminator