## 4 Results and Discussion  
Reference10 utilized LBP as feature description operators for defective CT and then trained an SVM classification. HOG(Histogram of Oriented Gradients) [7] is also often employed to describe features of defects. Therefore, LBP + SVM, HOG + SVM and the defect discriminator of this article are compared on the verification set simultaneously. The discriminator parameters are configured according to Section 3.2. LBP sets eight sampling points in a radius of 2, HOG sets 32 pixels per cell unit, four cells per block unit, and the number of histogram channels is 9. The following conclusions can be drawn from Table 6: the number of defect TP is lower than the reference TP, and the number of defect FN is greater than the reference FN, indicating that the probability of misrecognizing a defect as a texture is greater than that of misrecognizing a texture as a defect. In addition, the TP value of the three methods corresponding to the defect and the reference rectangle increases with the increase in the SN, while the FN and FP decrease accordingly. The conclusion that can be drawn is that our method has the highest detection accuracy rate for the defective boxes and the lowest false detection rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SN** & **Method** & **Items** & **True Number** & **TP** & **FN** & **FP** & **TN** \\ \hline \multirow{2}{*}{1} & LBP + SVM & Defective boxes & 781 & 557 & 224 & 219 & 562 \\  & Reference boxes & 781 & 562 & 219 & 224 & 557 \\ \multirow{2}{*}{2} & HOG + SVM & Defective boxes & 781 & 639 & 142 & 81 & 700 \\  & Reference boxes & 781 & 700 & 81 & 142 & 639 \\ \multirow{2}{*}{3} & \multirow{2}{*}{This paper} & Defective boxes & 781 & 761 & 20 & 11 & 770 \\  & Reference boxes & 781 & 770 & 11 & 20 & 761 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validated results from three methods. Figure 6: Comparison diagram of the evaluation indexes (**a**) and cost time (**b**) from the three methods. It is evident from Figure 6a that our algorithm is superior to the LBP + SVM and HOG + SVM methods in four evaluation indicators, and its accuracy rate reaches 98.02%, which is more than 13% higher than the other two methods. In ref. [8], re-judging the detection results by the discriminator can significantly improve the defect detection accuracy of the algorithm, which shows that the detection accuracy of CTs with three different textures is increased by 6.38%, 5.24% and 1.65%, respectively. Our discriminator has a better-detailed feature representation than these three methods and achieves a good defect discrimination effect. With such a significant improvement in our algorithm's accuracy, the misidentification of non-defective CTs as defects will be significantly reduced, saving enterprises production costs and allowing automated visual inspection technology to be used practically. In combination with the actual production situation, there may be multiple detection rectangles on each image, which takes too much time to discriminate and impedes the enterprise's production capacity. On the verification set, the comparison results of the detection time are obtained after five rounds of testing with three methods, as shown in Figure 6b. The first method takes an average time of 10.8 ms to distinguish each rectangular box, the second method is 8.1 ms, and ours is 12 ms. Nevertheless, the increased inspection time for the production of a single tile has minimal impact on the company's production capacity. Therefore, our method has significant advantages based on the comprehensive evaluation of the detection accuracy and efficiency of the rectangular frame discriminator. In addition to the comparison and verification of the discriminator, the effectiveness evaluation of the entire algorithm is more valuable for practical applications. The complete algorithm integrating the comparator, detector, and discriminator is verified on the test set and compared with the results of the independent detector.