# WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds
## 3 Results and Discussion
### Results and Discussion
This is due to the strong and efficient configuration defined with the stereoscopic setup in terms of images matching, photogrammetric reconstruction, and dense model generation. Furthermore, the constraint defined by the known baseline between cameras allowed us to work directly with metric models. This is not the case with the compact handheld camera, where the 3D models had to be scaled using artificial targets. On the contrary, it should be noted the greater flexibility and speed of acquisition with the compact handheld sensor, guaranteeing more forward speed and being especially suitable for non-expert operators in photogrammetry, since following a simple protocol of acquisition, the structure from motion approach can successfully solve the photogrammetric 3D model of the weld. The resolution and precision achieved by photogrammetry improved substantially when compared to the ones achieved by the methods currently used, based on the visual \begin{table}
\begin{tabular}{c c c} \hline  & **Photogrammetric** & **Compact Handheld Camera** \\  & **Stereoscopic Arm** & **Compact Handheld Camera** \\ \hline Resolution (mm) & 0.05–0.06 mm & \(\leq\)0.2 mm \\ \hline Precision (mm) & 0.10–0.15 mm & \(\leq\)0.4 mm \\ \hline \end{tabular}
\end{table} 
Table 1: Quality results of the photogrammetric 3D models obtained with the different cameras. Figure 6: Sample of the 3D photogrammetric models obtained with WELDMAP software. inspection of welds using manual gadgets (e.g., fillet weld gauge, cam gauge). These analogical methods allow verifying if the fillet is acceptable, but they do not allow technicians to record the geometry of the bead. Furthermore, they do not provide submillimetric accuracy and they are highly dependent on the inspector's skills and expertise. Alternative methods based on geotechnologies have been developed, for example in [53], where a laser-based machine vision system allowed for the monitoring and controlling of welding with a maximum error of 0.55 mm. In [54], a laser line scanner was employed to measure the surface profile of the weld bead. Despite it having a 0.015 mm spatial resolution, it only allowed for measuring a distance of 100 mm with a width of 24 mm. Besides, the classification between bead and base metals was unsatisfactory. Afterwards, and based on these photogrammetric 3D models, a semantic classification of the welds using geometric features and an unsupervised classification strategy based on Graph Cut was applied, taking three classes as input: left plaque (red colour), right plaque (yellow colour) and bead (blue colour). The axis extraction is outlined in white colour (Figure 7). It should be noted the accurate and successful results of the classification strategy based on geometric features and energy function. The energy model has a relatively simple formulation and provides convincing results in practice. Figure 7 shows the potential of the model on different types of welds. Trying to validate the results, we obtained an expert on welds who checked the results, inspecting the semantic 3D models visually. Once the welds were semantically classified, the quality control of welds was performed based on the four defects defined (Figure 2), using cross-sections as reference. Since the process is fully automatic, cross-sections were generated with 2.5 mm resolution. Figure 8 and Table 2 show the results of the defectological analysis of the welds assessed. Only those welds which present a defect are outlined in Table 2.  
In view of Table 2, the efficiency of the WELDMAP tool to detect anomalies automatically is clear. It should be noted that the tool allows you to configure the threshold of defects as well as their percentage to consider if a weld should be rejected. In this sense, it must be emphasised that the welds inspected in situ did not provide any defectology, while the welds analysed in the laboratory (no.