## 1 Introduction  
The surface of airport pavements must be kept in high-grade condition to ensure the movements of the aircraft are safe during take-off, landing, and taxiing. Aviation regulations require that the surface of the paved airport must be regularly monitored and evaluated for the occurrence of distress. This process, often called pavement stocktaking, is usually manually performed by visual inspection to identify and assess the signs of individual types of distress every year. Due to the size of the airport and the air traffic volume, this procedure often requires the airport authority to restrict air traffic, which makes the process time-consuming. The data collected during the inspection helps the airport authority to plan necessary maintenance and repairs. One efficient way to reduce the time and effort involved in the human inspection process is Unmanned Aerial Vehicle (UAV) imagery followed by a deep learning algorithm for object detection. Through aerial photography, an overview of a large area is obtained, and another point of view is obtained. The image quality and visible details then depend on the camera and flight altitude [1]. As a part of artificial intelligence, the deep learning framework creates a detector to recognize, locate, and label the desired distress, and computer vision methods such as image segmentation and morphological operations are often used for further distress analysis. The approach based on deep learning using convolutional neural networks has become practical for object detection due to its speed and accuracy. Object detection aims to replicate the ability of humans to recognize and identify objects in video or images by using a computer. Regions with Convolutional Neural Network (R-CNN) [2] is a two-stage detection algorithm that combines rectangular region proposals with convolutional neural network features. The first stage looks for a subset of regions that may contain an image object, while the second classifies the objects in each region. A Fast Region-based Convolutional Neural Network (Fast R-CNN) [3] improves training and testing speed while increasing detection accuracy, since the computations for overlapping regions are shared. The Fast R-CNN detector processes the entire image, unlike the R-CNN method, which only crops and resizes region proposals. A Faster Region-based Convolutional Neural Network (Faster R-CNN) [4] adds a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The RPN takes advantage of anchor boxes and generates region proposals faster and more accurately. A Single Shot Multibox Detector (SSD) [5] is a single-stage object detection method that applies a discretization algorithm to create a set of default boxes for detecting objects. The network then generates scores and adjusts the boxes based on the predicted object categories. The network also combines predictions from multiple feature maps to efficiently handle different sizes of objects. The SSD is considered faster than two-stage detectors, such as the Faster R-CNN detector. You Only Look Once (YOLO) is a single-stage object detector that excels in high speed and accuracy. YOLO was first published by [PERSON] et al. [6] in 2015 and has become one of the most popular detectors, even for pavement distress detection. The basic principle included a single neural network directly predicting bounding boxes and class probabilities from full images in one evaluation. Since then, the YOLO family has grown to include more variants as its performance has gradually increased. [PERSON] and [PERSON] introduced YOLO9000 and YOLOv2 in 2016, and subsequently, they announced an enhancement called YOLOv3 in 2018. [PERSON] et al. launched YOLOv4 in 2020. [PERSON] refined YOLOv4 and released YOLOv5 in the middle of 2020. [PERSON] et al. created the YOLOv6 series for industry-level applications in 2022. In the same year, [PERSON] et al.