## 2 Materials and Methods
### Equipment and Procedure  
A consumer quadcopter used in this project, a DJI Mavic 2 Enterprise Dual, was approved for military use by the Military Aviation Authority (MAA) of the Czech Republic. It features a fully stabilized 3-axis gimbal camera with a Forward Looking Infrared (FLIR) longwave thermal camera and a visual camera that simultaneously provides both infrared and visible light imaging. The visual camera has a 1/2.3\"3\" CMOS sensor and provides the maximum image size of 4056 \(\times\) 3040 (4:3) or 4056 \(\times\) 2280 (16:9) pixels, a field of view of 85\({}^{\circ}\), with an aperture of f/2.8 at infinity. The thermal camera has an uncooled Vox microbolometer sensor, which provides the maximal image size of 640 \(\times\) 480 (4:3) or 640 \(\times\) 360 pixels (16:9), a field of view of 57\({}^{\circ}\), with an aperture of f/1.1 at infinity [23]. The model was supplied with four intelligent flight batteries covering a flight time of approximately 20 min.  
The DJI Pilot application (Shenzhen, China), version v2.5.1.10, installed in the flight controller, was used for flight planning. After visual inspection, the runway was divided into four sections; thus, the region of interest had a rectangular shape. Three were intended as a source for creating the crack database, and the fourth was for testing and experimental purposes. The following flight parameters were set for mapping in the application. The width of the section corresponded to the width of the runway and the length of the expected flight time with one battery. The flight altitude was set to 25 m (82 ft) AGL, with the forward overlap at 80% and the side overlap at 70%. The flight trajectory was chosen to be parallel to the runway axis. This solution reduces the number of pivot points where the UAV changes the flight direction resulting in reduced speed and increased imaging time. The length of the mapped section could be extended this way, and the battery capacity could be better utilized.  
The captured overlapping images were stitched together using the commercial photogrammetric software Agisoft Metashpe Professional Edition (St. Petersburg, Russia) [24], Node-Locked Educational License, version v1.5.2.7838, to generate an orthomosaic map of each section of the runway. The first section of the runway consisted of 383 images, the second consisted of 391 images, the third consisted of 393 images, and the fourth (test and experimental) section consisted of 394 images. The images were processed with the following workflow steps. The image alignment to build a sparse point cloud: accuracy was set to low, generic preselection was enabled, the key point limit was set to 40,000, the tie point limit was set to 4000, and the adaptive camera model fitting was enabled. The dense point cloud building: quality was set to low, depth filtering was set to mild, and calculate point color was enabled. The source data for mesh building was a dense cloud, the surface type height field with low depth maps quality. The digital elevation model was built using a dense point cloud with enabled interpolation. The low processing parameters were set due to faster image processing. The orthomosaic was built using mosaic blendingmode; as a surface was used, a digital elevation model and hole filling were enabled [1]. The Ground Sample Distance (GSD) of the first section of the runway was calculated to be 8.52 mm/pix, the second section to be 8.58 mm/pix, the third to be 7.02 mm/pix, and the last, the test section, to be 6.41 mm/pix. The different altitudes of the individual runway sections caused the difference in GSD values.  
Data plotting, calculations, and YOLOv2 detector training were computed in the programming language and numeric computing environment Matlab, using a computer with the operating system Windows 11, an Intel Core i9-9980 HK CPU @ 2.40 GHz, 32 GB RAM, and an NVIDIA(r) GeForce(r) RTX 2060 graphic card.