###### Abstract  
Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities. road cracks; UAV; deep learning; target detection; road damage evaluation; framework +
Footnote †: journal: Medicaltheir intelligent systems [4]. [PERSON] et al. [5] utilized core components such as on-mounted high-definition image sensors, laser sensors, and infrared sensors, etc. These components enable the acquisition of high-precision road crack data in real-time. However, the overall configuration of the vehicle-mounted system is expensive and limited in scope, making it challenging to widely apply [2]. Notably, automatic pavement distress inspection has traditionally utilized image-processing techniques such as Gabor filtering [6], edge detection, intensity thresholding [7], and texture analysis. Cracks are identified by analyzing the changes in edge gradients and intensity differences compared to the background, and then extracting them through threshold segmentation [2]. However, these methods are highly influenced by environmental factors, including lighting conditions, which can affect their accuracy. Moreover, these methods are not effective when the camera configurations vary, making their widespread use impractical [1; 8]. Given the limitations of these traditional approaches, it is crucial to develop a cost-effective, accurate, fast, and independent method for the accurate detection of road cracks. In recent years, there have been significant advancements in machine learning and deep learning algorithms, leading to the emergence of automatic deep learning methods as accurate alternatives to traditional object recognition methods. These methods have shown immense potential in visual applications and image analysis, particularly in road distress inspection [1; 8]. [PERSON] et al. [9] proposed a deep convolutional neural network (CNN) architecture for image classification, especially in the detection of distresses in asphalt pavements. [PERSON] et al. [3] presented an attention-based crack network (ACNet) for automatic pavement crack detection. Extensive experiments on the CRACK500 demonstrated that ACNet achieved a higher detection accuracy compared to eight other methods. [PERSON] et al. [10] utilized a supervised machine learning network called RetinaNet to detect and classify various types of cracks that had developed in asphalt pavements, including lane markers.