## 1 Introduction
Furthermore, other applications for crack detection and description have been developed. For instance, a unique algorithm called MorphLink-C was developed to analyze images of concrete structures captured by UAVs. It visually analyzes the high-resolution data and finds the pixels of the separated and grouped cracks. The number of pixels was used to calculate the crack width, which was then converted into millimeters. The difference between the computed width and the actual survey was from 3% to 50%, which met the limit survey error range, which was set to 0.1 mm [19]. I-Crack is a tool that detects and measures the length and width of reinforced concrete cracks in high-resolution panoramic images. It was built on algorithms that are commonly used in Matlab. The program consists of four main building blocks: (1) an algorithm for detecting edges, (2) morphological operations for filling cracks, (3) the separation of cracks from surface defects, and (4) the measurement of the crack width. I-Crack was evaluated using crack patterns and widths manually collected from the tests of nine walls to ensure that the algorithm correctly detected cracks, followed by comparing the predicted width. The maximal difference between the I-Crack and the manual measurements was 0.48 mm, which was considered insignificant [20]. The movement area of an airport is defined as a part of an aerodrome to be used for the take-off, landing, and taxiing of aircraft, consisting of a maneuvering area and apron(s). The maneuvering area is part of an aerodrome for take-off, landing, and taxiing aircraft, excluding aprons. A runway is a defined rectangular area on a land aerodrome prepared for landing and aircraft take-off [21]. The runway is considered a critical element of maneuvering areas, the surface of which is usually built of concrete or asphalt at civil and military airports. One of the most common types of airport pavement distress is a crack, from which other types of distress can be further derived according to their orientation, location, and length within a slab in the concrete surface. The width of a crack is considered the most crucial factor that affects air traffic safety. Depending on the severity, the crack can be categorized into three levels: small (the average crack width is less than or equal to 0.2 cm), medium (the average crack width is greater than 0.2 cm and less than 1.0 cm), or large (the average crack width is greater than or equal to 1.0 cm). According to North Atlantic Treaty Organization (NATO) terminology in determining airfield pavement conditions through visual surveys, longitudinal, transverse, and diagonal cracks correspond to distress code 63 [22]. The main goal of this article is to present an automated system for the automatic detection and evaluation of distress on a concrete runway through artificial intelligence using aerial imagery. The system includes runway image acquisition through UAV, captured image data processing for dataset creation, crack labeling, deep learning model design based on the YOLOv2 object detector, its training, and performance evaluation. The system was tuned until the required accuracy was reached and was sequentially deployed into live operation. Once the crack was detected, its position and size were computed by counting individual pixels in a binary image (Figure 1). The transverse crack was selected as the main component to verify the functionality of the system. Although newer versions of the YOLO detector have introduced many improvements in accuracy and functionality, the popular YOLOv2 was chosen for this project. YOLOv2 is generally faster than later versions due to its simpler architecture and fewer layers, which allows it to process images more quickly. The simple architecture is also beneficial for easier network customization for a specific use case. While later versions of YOLO have improved the accuracy, the differences are not significant enough to justify the additional computational cost.