Figure 6. (a) The blue points represent the mapped paint
damages found in the drone images captured in one drone flight. A CAD
model moved to the same position as the geo-referenced coordinate system
is used (b) The paint damages have been divided into 7 different
clusters. The surface area for the clusters is presented together with
image numbers. The image number makes it possible to find the images
that have paint damages that contributed to the paint damage points in
the cluster. (c) The blue points represent the paint damages mapped to
the reconstructed 3D model.
The mapping method where the paint damage pixels are projected along
surface normals is, as stated above, more accurate than the closest
point on the TP method. The former method is needed when the paint
damage is contained in a small area. The closest point approach would
require a very high mesh resolution of the TP. Figure 7 shows an example
where the paint damage has a small surface area. Figure 7 (a) shows the
content of the bounding box calculated using the YOLO algorithm. The
paint damage has a light yellow color and is placed near the center of
the bounding box. Figure 7 (b) shows the output of the color threshold
segmentation algorithm. The result of mapping the black paint damage
pixels to the TP is presented in Figure 8 (a).