## 5 Evaluation Strategies and Performance Metrics  
Evaluation plays an important part in NDT-based proposed methods towards validation, and it can be classified under _quantitative_ and _qualitative_ approaches. The simplest and most straightforward method is qualitative evaluation (mainly visual), which depends on the experience of the operator performing it. The aerospace industry has gradually moved towards both qualitative and quantitative evaluation, relying less on the operators' skills and expertise [117]. Noting that NDT inspection is regarded as a quantitative analysis method, many papers in the current literature pay close attention to evaluation (and the use or proposal of relevant evaluation _metrics_) to support the efficacy of their approaches. We aim to systematise the presentation of evaluation strategies in this section, providing critical insights into NDT image fusion-related efficacy. A useful example of the importance of evaluation in NDT-based inspection is in a highly noted recent paper [118], which looked into barely visible impact damage (BVID) in composite materials. The authors used NDT fusion of ultrasonic testing and X-ray computed tomography. Their particular fusion approach is shown graphically in Figure 9a, with a particular aim of investigating the true mapping of the defect from UT to the true defect from X-ray CT. (Figure 9b illustrates the scanning views obtained for the defect.) Their particular metric was the distance from the defect's centre to the boundary points  
Figure 8: Schematic representation of D-S belief probability calculations for a random point using its local neighbourhood on the defect-free region on the global distribution of feature matrix (Positive evidence is where there is a defect, negative evidence is where it is non-defective, and doubt is where there is a high plausibility.) [115]. (see the example in Figure 9c, which illustrates a form of pseudo-segmentation of the defect area). The authors included statistical analysis to showcase the differences between the UT C-scans and B-scans relative to the defect mapping (estimation accuracy of the BVID), with the C-scan providing better overall results, as claimed by the authors of the work. An interesting outcome from this paper to consider is the transition from a qualitative aspect of defect analysis (images) to the statistical analysis of the boundary point distance (quantitative) viewpoint. Given that defect analysis, detection and classification can be a very challenging task (depending on the nature and importance of the defect), researchers looked into a variety of evaluation metrics to support NDT analysis. There is no unique evaluation metric that can be applied in this domain (as is the case in many other engineering domains), with a number of evaluation criteria (metrics) introduced to facilitate a more informed outcome decision in NDT analysis of defects [119]. We present a series of important evaluation metrics (merely exhaustive) to assist the readers in understanding their nature, use and potential impact in NDT fusion works. Common evaluation metrics seen in the literature are the _root mean square error_ (RMSE), _signal-to-noise ratio_ (SNR), _correlation coefficient_ (CC), _(fusion) mutual information_ (MFI) and _structural similarity index_ (SSIM). It is possible to mention more metrics, but many of them are fairly correlated with each other, as can be seen with the 27 different metrics in [120]. Papers refer to these for evaluating the performance of fusion methods. In particular, a recent paper [119] closely utilised these criteria for their work on NDT fusion for defect analysis in Si-PV cells and comparison of the best algorithm outcomes. Figure 9: **(a)** Katunin-et-al. example fusion approach (block diagram). **(b)** Detected damage for fusion of X-ray CT scan (red) and UT C-Scan (blue color). **(c)** Distance calculation of boundary points to the centroid for X-ray CT and UT C-scan [118].