## 5 Conclusions  
In order to improve the detection accuracy of surface defects for CT, it is necessary to deal with the feature expression and measurement of complex background textures and defects. It is an open-set detection problem because of the variety of tile textures, complex patterns, and similar features to certain defects. We have proposed an effective algorithm to improve the accuracy of ceramic tile surface defect detection. It can solve this open set problem and obtain the following conclusions: \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Number of Defects** & **Background Texture of CTs** & **Defect Discrimination** & **Evaluation Index** \\ \hline
[1] & 5 & Simple textures & Without & Accuracy: 83\% \\
[6] & 3 & Without & Without & Accuracy: 93.4\% \\
[7] & 3 & Without and monochromatic & Without & Accuracy: 99.01\%, \\  & & One texture, two textures, & & texture: 93.2\% \\
[8] & 6 & three textures & With & Accuracy: 98.4\%, 96.2\% and \\  & & & & 91.21\% \\
[9] & 2 & Black Random Stripes, brown & & \\  & & light stripes & Without & F1-score: 92.36\% and 88.66\% \\
[10] & 1 & Without & Without & Accuracy: 87.5\% \\
[12] & 2 & Gray and colored textures & Without & Highest accuracy: 92.6\% \\
[13] & 4 & Without & Without & Accuracy: 94\% \\
[14] & 2 & Complex textures & With & Accuracy: 94.2\% \\
[15] & 2 & Simple textures & Without & F1-score: 94.8\% and 87.6\% \\  & & & Precision: 99.9\%; \\
[16] & 1 & Simple textures & Without & Recall: 78.7\%, F1-score:88\% \\
[17] & 1 & Simple textures & Without & Accuracy: 89.9\% \\
[18] & 1 & Simple textures & With & Accuracy: 99.43\% \\ The & 14 & Complex textures & With & The average accuracy: 98.19\%, \\ CNFA & & & & The average F1-score: 98.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with previous researchers’ results. Figure 7: Comparison diagram of the evaluation indexes (**a**) and cost time (**b**) from the two methods. * A CNFA for surface defect detection combining a comparator, detector, and discriminator is proposed. Collect the non-defective image to construct the negative sample, retrieve the reference image with the help of the comparator; obtain the result rectangular frame of the detection image through the detector, and obtain the corresponding reference rectangular frame. Finally, the discriminator judges the true and false defects of the resulting rectangle to improve the detection accuracy;
* Based on the modified VGG16 network, the 1024-dimensional feature vector of the detection image and the negative sample set is extracted, and the Pearson correlation coefficient is used to measure the distance between each other, and the corresponding image with the smallest search distance is the reference image;
* The detector is composed of Resnet101 + FPN + Cascade R-CNN network structure. According to the coordinates of det_boxes obtained through the detector, the corresponding box on the reference image is magnified by@times. Then, the coordinate box of the maximum correlation coefficient is obtained by the correlation coefficient matching method, which is the ref_box;
* The discriminator is composed of the modified MobileNetV3-Large as the backbone, combined with neck and head parts. The loss function based on the combination of Arc-margin and CE-Loss improves the network's feature discrimination ability of defects and background textures. Then calculate the cosine distance of the feature vectors of det_boxes and ref_boxes; the smaller the result, the greater the texture probability, otherwise it is a defect;
* After comparing with other methods on the verification set and test set, the accuracy and efficiency of our algorithm are proven. On the verification set, the accuracy of the discriminator reached 98.02%; on the test set, the average accuracy of the entire algorithm reached 98.19%, and the detection time of a single tile only increased by 64.35 ms.