## 1 Introduction
(the same research team that introduced YOLOv4) developed YOLOv7 [7; 8; 9; 10; 11; 12]. Many studies dealing with automatic distress detection and evaluation have recently been published. These studies primarily focus on road distress with asphalt or concrete cover, defects on bridge structures or dams, disruption in building walls, and to a minor extent, distress on airport pavements. In most cases, they target cracks as the basic type of distress. Similarly, the methods of image acquisition vary, from using a smartphone, a digital camera, a digital camera mounted on a moving vehicle, aerial (UAV) imagery, multi- or hyperspectral imaging to laser scanning, or the application of the light detection and ranging (LiDAR) method. [PERSON] et al. [13] proposed inspecting the runway and other movement areas using the UAV during airport operations instead of a controller driving a car and looking for dangerous objects. The advantages of the UAV over the conventional method are its flexibility and reduced time. The control processes are faster and can be executed more often. In addition, the data backup and evaluation of small surface cracks are also beneficial. [PERSON] et al. [14] conducted a study evaluating the applicability of UAVs for road-way pavement condition analysis. The core image dataset was collected during the flights of UAVs at Fitchburg Municipal Airport, USA, and was analyzed using two deep learning methods (SSD Mobilenet_V1 network, U-Net) and CrackIT toolbox. The deep learning methods outperformed CrackIT and generated satisfactory crack detection results. While the SSD Mobilenet_V1 network highlighted the detected cracks with rectangular boxes, U-Net could spot cracks at the pixel level, which is necessary for pavement condition index (PCI) values calculation. CrackIT is a toolbox created in the Matlab environment, which detects road pavement surface cracks distresses and characterizes them into types based on image processing and pattern recognition. Although the CrackIT tool can provide good characterization and detection results, dealing with fragile cracks, which are less than 2 mm wide, can be challenging. This is due to the difficulty in distinguishing them from traveling distresses. However, CrackIT aims to detect cracks at least 2 mm in width, in line with the guidelines provided by road experts [15]. [PERSON] et al. [16] gathered 9053 road images taken from a vehicle-mounted smartphone, which were split into 7240 for training and 1813 as testing samples. The types of cracks found in the road images were categorized into linear cracks (longitudinal, lateral), alligator cracks, and other corruptions. The YOLOv2 detector was used for automatic crack detection and transfer learning with ResNet-101 to speed up the training process. It was investigated that other deep learning frameworks, such as YOLOv3, SSD, or Faster R-CNN, failed to achieve the same score rate as YOLOv2 in the same samples. [PERSON] et al. [17] compiled a dataset consisting of road images captured from two camera views of an identical pavement segment, i.e., wide-view and top-down-view angles, each of 7237 images. The wide-view angles were used to classify distresses, while top-down-view images were used for calculating their density. The images consisted of distress types critical for pavement condition assessment, such as reflective, transverse, block, longitudinal, alligator, sealed transverse, sealed longitudinal, and longitudinal lane cracking, along with potholes. The dataset was evaluated using the YOLOv2 and Faster R-CNN boosted by transfer learning, where YOLOv2 resulted in more accurate distress characterization than the Faster R-CNN model. [PERSON] et al. [18] compared the YOLOv5 model of versions YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x for road crack detection based on 3001 images taken by a digital camera depending on the detection rate and detection efficiency. The individual variants of YOLOv5 were evaluated on 640 \(\times\) 640 images, where YOLOv5s had the best trade-off between speed and accuracy.