# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 4 Results and Discussion
### Detectability of Sub-Surface Defects in Front Perspective
In contrast to ROI A, however, a very homogeneous temperature distribution can be seen in the center of the specimen. Defects 5 to 7, which are located below the surface, do not distinguish themselves by a local temperature maximum. Even by image filtering in Figure 7c, only defect 6 is detected. Note also that false detections occur during the evaluation, due to the local temperature distribution on the surface of the specimen in the defect area no longer being distinguishable from the temperature distribution of the surrounding area. On the one hand the defects are located deeper in the material and on the other hand, the material is inhomogeneous and natural accumulations of resin could be detected as defect incorrectly. Thus, artefacts caused by cross-sensitivities here falsely appear as detected defects. While defect 6 is detected in the pre-processed data in a large part of the measurements from test series 2, defect 5 is only detected in 3 of 35 tests (at \(k=22\,\)mm and \(t_{\text{exc}}=30\,\)s, at \(k=43\,\)mm and \(t_{\text{exc}}=20\,\)s, at \(k=114\,\)mm and \(t_{\text{exc}}=15\,\)s), defect 7 not once. At this point, the contrast enhancement by means of PCA is an option. In Figure 7d, the effect of PCA is already visible just after the pre-processing. Defect 6 clearly stands out as a local maximum in temperature compared to the raw image. Defect 5 is also slightly visible, while defect 7 is barely visible. The PCA-based image with filtering as well as the automatic defect detection and the calculation of the CNR in Figure 7e shows that all 3 defects are detected as such. As in the reference image without filtering, false detections occur at the edges. Due to the low contrast of defect 7, the threshold for this automatic detection had to be low as well. Changing the threshold to a higher value results in the disappearance of the falsely detected artefacts. However, a higher threshold value also leads to defect 7 not being detected by the algorithm even though it is visible in the processed data to the human observer. While defect 5 and 6 have depth-to-diameter ratios of 0.69 and 0.73, respectively, and are thus within the maximum detected ratios of current research, even defect 7 is detected with a depth-to-diameter ratio of 1.04. This confirms the current view in the literature that defects with a depth-to-diameter ratio of \(\geq\)1 are difficult to detect. Figure 6: Results of test series 1 (ROI A, \(t_{\text{exc}}=25\,\text{s}\), \(t_{\text{rec}}=15\,\text{s}\), \(k=89\,\text{mm}\), (**a**) surface temperature of the whole camera FOV, (**b**) result for the reference method without filtering at \(t_{\text{rec}}=0\,\text{s}\), (**c**) reference image with filtering, automatic defect detection and calculation of the CNR, (**d**) result for the PCA-based method without filtering, (**e**) PCA-based image with filtering, automatic defect detection and calculation of the CNR. In the results from test series 2, it should be noted that defect 6 stands out significantly more from its environment than defect 5, although defect 6 has a smaller diameter and a higher depth-to-diameter ratio, which means that the defect should be less visible according to the considerations from test series 1. The reason for this must therefore be the depth at which the defects are located. Defect 6 at 2.2 mm depth is closer to the surface than defect 5 at the depth 2.4 mm. While the difference between the two defects in depth is only 0.2 mm, the difference in diameter is 0.5 mm. It can be concluded from this that the distance between the surface and the defect has to be more important than the defect diameter in the detection of defects by thermography, besides the overall relationship between defect depth, thermal transfer time and observation period. A possible explanation is found in the thermal properties of GFRP. Since it is an anisotropic material, the thermal conductivity is directionally dependent [34]. Here, the heat preferentially propagates in the fiber direction, especially within one layer.