###### Abstract
BL-YOLOv8 surpasses other mainstream object detection models, such as Faster R-CNN, SDD, YOLOv3-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, by achieving detection accuracy improvements of 17.5%, 18%, 14.6%, 5.5%, 5.2%, 2.4%, and 3.3%, respectively. Furthermore, [PERSON] et al. [21] proposed an improved CNN method based on UAV imagery, demonstrating a remarkable accuracy of 99.04% on a customized test datasets. Lastly, [PERSON] et al. [22] proposed a highway crack detection and CrackNet classification method using UAV remote sensing images, achieving 85% and 78% accuracy for transverse and longitudinal crack detection, respectively. These aforementioned studies primarily aim to enhance the deep learning algorithm using UAV images. This enhancement improves the accuracy of road crack detection and also establishes the methodological foundation for the crack target recognition algorithm discussed in this paper. However, most of the above-mentioned studies primarily focused on UAV detection algorithms and neglected UAV data acquisition and high-quality imagery integrated into detection methods. For instance, the flight settings required for capturing high-quality images have not been thoroughly studied [2]. Flying too high or too fast may result in poor-quality images [22]. [PERSON] et al. [2] and [PERSON] et al. [16] both introduced flight setup and experimental tricks for efficient UAV inspection. [PERSON] et al. [23] proposed a systematic solution for automatic crack detection for UAV inspection. These studies are still incomplete due to a lack of detailed data acquisition and pavement distress assessment. Additionally, there is a lack of quantitative measurement methods for cracks, which hampers accurate data support for road distress evaluation. Furthermore, inconsistency in flight altitude and the absence of ground real-scale information of cracks adversely impact the subsequent quantitative assessment of cracks. Obviously, existing studies frequently lack a systematic solution or integrated framework for UAV inspection technology, which hinders its widespread application in pavement distress detection. Therefore, this study aims to propose a formal and systematic framework for automatic crack detection and pavement distress evaluation in UAV inspection systems, with the goal of making them widely applicable. Our proposed framework for a UAV inspection system for automatic road crack detection offers several advantages: (1) It demonstrates a more systematic solution. The framework integrates data acquisition, crack identification, and road damage assessment in orderly and closely linked steps, making it a comprehensive system. (2) It exhibits a greater robustness. By adhering to the flight control strategy and model deployment scheme, the drone ensures high-quality data collection while employing state-of-the-art automatic detection algorithms based on deep learning models that guarantee accurate crack identification. (3) It presents an enhanced practicality. The system utilizes the cost-effective DJI (DJ-Innovations Company, headquartered in Shenzhen, China) Min2 drone for imagery acquisition and DL-based model deployment, making it an economically viable solution with significant potential for widespread implementation. The rest of this paper is organized as follows: Section 2 presents the framework for the UAV inspection system designed specifically for pavement distress analysis. In Section 3, we provide a comprehensive overview of four prominent deep-learning-based crack detection algorithms, namely Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s, along with their distinctive characteristics. Section 4 elaborates on the well-defined procedures employed for UAV data acquisition and subsequent data reprocessing. The experimental setup and comparative results are presented in Section 5. In Section 6, we propose quantitative methods to evaluate road cracks and assess pavement distress levels. Finally, in Section 7, we summarize our research while discussing future work.