## 3 Results and Discussion
### Transverse Crack Size Determination
Figure 13: (**a**) A grayscale image containing a transverse crack cropped from an orthomosaic based on the predicted bounding box coordinates as an example of two homogeneous objects; (**b**) The corresponding histogram with only a significant single peek. Each bounding box containing the transverse crack was split into blocks of the same size, in which the area, centroid, and centerline length of the crack were computed. The length of the transverse crack was measured as the Euclidian distance between the individual points; i.e., between the maximum boundary points through the centroids of each block (Figure 15). The width was measured as a ratio of the area of the transverse crack to its axial length in each block, and the average value was then calculated across all blocks. All of the measurements in the binary images were performed in pixels. The spatial resolution of images was kept the same; thus, the dimensions in pixels were multiplied by the GSD value (Table 4) to obtain the size of the transverse crack in mm (cm). The difference in crack size, depending on the number of blocks into which the bounding box was divided, is shown in Table 5.  
The number of blocks into which the bounding box can be divided affects the accuracy of the crack measurement, where the higher number of blocks improves the accuracy. The maximal Feret diameter \(\mathrm{d_{Fmax}}\) might be a good and quick approximation for the straight cracks for the length measurement, whereas the minimal Feret diameter \(\mathrm{d_{Fmin}}\) is not convenient for the width measurement due to the emplacement of tangents in the case of zigzag cracks. Figure 14: The workflow of preparation of the transverse crack for the dimension measurement: (**a**) The image cropping based on the coordinates of the predicted bounding box from the YOLOv2 detector; (**b**) Conversion to the grayscale image; (**c**) Image sharpening; (**d**) Image binarization using [PERSON]’s thresholding method; (**e**) Crack extraction from a binary image; (**f**) Filling holes within the crack. Figure 15: The transverse crack length measurement within the binary image of size \(29\times 95\): (**a**) The division into nineteen blocks each of size \(29\times 5\); (**b**) The division into five blocks each of size \(29\times 19\); (**c**) Application of the maximal Feret diameter \(\mathrm{d_{Fmax}}\). The YOLOv2 detector detected a total of 609 transverse cracks on the experimental section of the runway, which were further processed using the methods mentioned above. The final output of this study contains the summary of computational and image data. The computational data comprises a list containing the serial numbers of the concrete slabs within a runway and their associated vertical cracks. For each vertical crack, the following information is available: serial number, the bounding box coordinates [x, y, width, and height] within the concrete slab, the position within the concrete slab, i.e., the corresponding quadrant (edge, corner), the outcome of the dimension measurement (length and width), and the severity in relation to flight operations (small, medium, or large). The image data comprise the orthomosaic in which bounding boxes with the transverse crack are marked. The resolution (Table 4) of the orthomosaic (Figure 7) and its size, which reaches 63.8 MB, requires a high-performance computer to view. Thus, a simple vector graphic containing all of the essential image data was created to view the information even on less powerful computers. This graphic contains a grid of marked concrete slabs, where the bounding boxes with the transverse crack are displayed and labeled (the serial number of the crack). A detail of this graphic can be seen in Figure 11. The last way the image data