## 4 Conclusions  
This study focused on detecting and evaluating transverse cracks in the concrete runway of airports using a combination of UAVs and a well-known YOLOv2 detector. It was observed that the Adam optimizer reduced multiple detections in one object and slightly improved detection accuracy. The detection evaluation metrics of the YOLOv2 detector, such as Precision, Recall, and F1 Score, are related to the subjectively set threshold for a specific task, which is considered a successful match. Several options could enhance detection accuracy. For instance, increasing the training images dataset, setting the flight altitude to a lower level to gain more detailed images for YOLOv2 detector training and evaluation, hyperparameters adjustment, the layer modifications in ResNet-50, or using another feature extraction network. In addition to detection accuracy, the YOLO deep learning frameworks also take advantage of the ability to perform real-time detections. The speed of detecting an object in an image is progressively improving with new YOLO variants. This feature was not realized in this study; nevertheless, it might be beneficial since 8316 image blocks of size 224 \(\times\) 224 had to be processed to cover the whole experimental section of the runway.  
As part of evaluating transverse cracks, their position in the concrete slab was surveyed using the developed coordinate system. We can state that the location of particular transverse cracks was accurately determined over a large airport area due to the concrete slab marking system, its division into quadrants, edges, and corners, and YOLOv2 predicted bounding boxes. The location of the cracks can also be determined using GPS coordinates; however, given the accuracy of the GPS devices, which is approximately 5 m [39] of true position in the open sky, and the graphical outcomes presented, this was judged as unnecessary.  
The transverse crack dimension measurement was performed within the predicted bounding boxes. The critical step in this process was defining which pixel belonged to the background and which formed the transverse crack. Although a comparison of calculated and actual crack sizes has not yet been made, Figure 14a,f shows that the binarized transverse crack is wider than in reality. A more detailed image could improve this issue, and the dimension measurement would be more precise. Related to this is the need to recognize cracks of 0.2 cm or less in width, which is impossible with the UAV used, even at lower flight altitudes.  
The main contribution of this paper is to replace humans in the field of inspections and the control of paved airport surfaces by UAVs with the subsequent application of artificial intelligence. Artificial intelligence has been shown to imitate human abilities in locating cracks and to independently decide whether a crack is present. Unlike other works that only concluded distress detection with a certain probability, a system is developed here, automating the process in perspective.  
Future work will focus on expanding the database to include other types of distress and more detailed crack evaluation to obtain the Pavement Condition Index (PCI) as the numerical indicator of runway (taxisway, apron) condition.  
Methodology, L.C.; Software, J.M.; Validation, L.C.; Formal analysis, J.M.; Investigation, J.M.; Resources, L.C.; Data curation, [PERSON]; Writing-original draft, J.M.; Project administration, L.C.; Funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their sensitivity and potential confidentiality concerns.  
**Conflicts of Interest**: The authors declare no conflict of interest.