## 4 Results and Discussion
### Defect Detectability and SNR Evaluation  
Since circular defects were simulated, the roundness metric was calculated and evaluated to verify that circular defects were accurately detected. Roundness metric evaluation was applied to evaluate the shape of defects detected in the binarized image. The closer the roundness metric evaluation is to 1, the closer it is to being circular. Roundness is determined by the area and perimeter of the defect and is defined as Equation (14) [30]. \[Roundness\ metric=\frac{4\pi\times area}{perimeter^{2}} \tag{14}\]  
Tables 3 and 4 show the results of calculating the roundness metric of defects detected in temperature and phase binarization images according to whether or not the ATC compensation technique was applied. Overall, the amplitude showed better results than the phase for detected defects. In the phase binary image, the overall result value is over 67% (0.67), showing that it is evaluated as close to a circular shape. Amplitude binary images showed higher result values of over 0.75 overall, with a maximum score of 85% (0.85). However, in the phase binarization image, the round metric values for the A7 and C6 defects in the results without ATC applied and the B9 and C7 defects in the results with ATC applied were calculated as very low or high round metric values, which are not considered appropriate to evaluate because they were detected very small. Figure 13: Phase binarization image by Otsu algorithm: (**a**) not ATC applied, (**b**) ATC applied. A signal-to-noise ratio (SNR) evaluation was conducted to analyze the effect of improving detection probability by applying equivalent heating and compensation methods. In non-destructive testing, the ratio of defective signals to sound signals is used as a primary evaluation method and is a key factor in evaluating the detectability and uncertainty of defects. SNR can be expressed as Equation (15) [29]. \[\text{SNR}=20 log_{10}\ \frac{|ROI_{D,mean}-ROI_{s,mean}|}{\sigma} \tag{15}\]  
where \(ROI_{D,mean}\) is the arithmetic means in the defective area, \(ROI_{s,mean}\) is the arithmetic means in the sound area, and \(\sigma\) is the standard deviation of the sound area. To quantitatively analyze the SNR improvement, an area was selected based on defects detected in the phase binarization image. Figure 14 shows the SNR trend according to the application of ATC and TRC compensation techniques. SNR was evaluated using temperature images to analyze the improvement of each technique. Additionally, to calculate SNR, the values of 3 \(\times\) 3 pixels in the center of the defect area and 3 \(\times\) 3 pixels in the adjacent sound area were applied. Figure 14a shows the SNR trend before and after applying ATC and TRC according to the defect depth as a graph and first-order trend equation. As a result, by applying the compensation technique, the original R\({}^{2}\) value increases from 0.9232 to 0.9987, improving the proportionality between the depth of the defect and the calculated SNR value. Figure 14b shows the SNR trend before and after applying ATC and TRC according to the defect diameter as a graph and first-order trend equation. Similarly, it can be seen that the original R2 value increases from 0.9069 to 0.9503 through the application of the compensation technique, improving the proportionality