# WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds
## 3 Results and Discussion
### Results and Discussion  
The results were obtained by applying the workflow outlined in Figure 1 and the defectology analysed, described in Figure 2, taking multiple RGB images as input data. These images were acquired using a photogrammetric stereoscopic arm composed of two reflex cameras (Canon EOS 700D) with two macro-lenses (EF-S 60 mm) (Figure 4). Trying to check the robustness of the feature extraction and matching process codified in WELDMAP, we used also images acquired with a compact handheld camera (Panasonic Lumix DMC-FZ38). In this case, images were acquired by varying their distances and perspective to reinforce the invariability of the detectors and descriptors. Subsequently, the images were uploaded to WELDMAP. At each stage of the process (Figure 1) the final user can choose among different algorithms. In order to help the final user to choose among them, the platform incorporates integrated help for the different application cases. In order to improve the feature extraction and matching (Figure 5), all input images were pre-processed based on the same algorithm: recursively separated and weighted histogram equalisation (RSWHE) [31], since RSWHE preserves the image brightness more accurately and produces images with better contrast enhancement. All employed detectors were limited in the maximum number of keypoints (5000) and the used matching strategy was robust matching, supported by RANSAC using the fundamental matrix as a geometric test [25]. All the computations were performed by exploiting parallel and GPU capabilities of the hardware. This setup was enough for providing good matches between images, even in those cases with more geometric and radiometric variations between images (dataset coming from a compact handheld camera). Figure 3: Different types of metallic welds acquired in real scenarios and in the laboratory. The best combination was obtained by experimenting with the different welds considered, so that the end user is offered this configuration. For these cases, an MSD detector [36] combined with an SIFT descriptor [33] was the best combination. For the stereoscopic dataset, the SIFT, supported by the GPU [52], provided very good results. For these cases, the MSD detector [36] combined with SIFT descriptor [33] was the best combination. For the stereoscopic dataset, the SIFT supported by GPU [52] provided very good results. Once images were connected based on matchings, an orientation and self-calibration of the different sets of images were performed combining triangulation supported by the DLT and the BA using the collinearity condition applied in an incremental way. Likewise,  
Figure 4: Stereoscopic photogrammetric arm composed by two reflex cameras (Canon EOS 700D) with two macro-lens (EF-S 60 mm). Figure 5: Feature extraction and matching in WELMAP software. In red are highlighted the different parts of the interface: menu (1), toolbar (2), tree view of the different inputs and intermediate results of the projects (3), view of the individual images (4), pop-up window with the matching results (5), console (6), and thumbnails (7). this registration process allowed us to invalidate the feature extraction and matching, since a wrong feature extraction and matching would entail an incorrect orientation and self-calibration of images. Finally, a dense model of the welds was provided based on the method described above. For the dataset coming from the compact handheld camera, the 3D models of welds were scaled based on a known distance using a codified target as reference. This was not the case for the stereoscopic dataset, since the point cloud are directly scaled based on the known and calibrated baseline. The following figure (Figure 6) outlines the photogrammetric 3D models of welds obtained, as well as the quality of the photogrammetric 3D models generated (Table 1). As it was expected, higher quality can be observed in both spatial resolution and precision with the photogrammetric stereoscopic arm.