## 7 Discussion  
This study proposes a comprehensive and systematic framework and method for automatic crack detection and pavement distress evaluation in a UAV inspection system. The framework begins by establishing the flight parameter settings and experimental techniques to enhance the high-quality imagery using the DJI Min2 drone in real-world scenarios. Additionally, a benchmark dataset was created and has been made available to the community. The dataset includes important information such as the \(\mathbf{GSD}\), which is  
Figure 12: Visualization results of crack detection and pavement distress evaluation. essential for evaluating pavement distress. In this experiment, our self-made crack dataset demonstrated its superiority compared to existing datasets used in similar algorithms, achieving the highest accuracy in crack recognition and algorithmic efficiency. The experimental result (refer to Table 6) revealed the significance of data acquisition quality in the accuracy of crack target recognition, with high-quality image data from the UAV imagery effectively improving the recognition accuracy. In this experiment, the detection capability for road cracks in a UAV inspection system could be enhanced through a range of strategies. Firstly, adhering to a drone flight control strategy ensured a consistent high and stable speed during data acquisition on urban roads. This guaranteed the collection of clear and high-quality drone images with attached real spatial scale information for distress assessments. Secondly, the sampling 'divide and conquer' strategy for model training and target detection involves various key steps, including 'the frame extracting from video and image cropping for large image' and'model learning and crack detection for small images', as well as 'fusion and splicing from small images'. This approach effectively improves the accuracy of identifying cracks in large-scale images while enhancing the operational efficiency of these models. Thirdly, the deployment of drone detection algorithms using both 'online-offline' and 'online-online' strategies provides flexibility based on different scenarios. The 'one-stage' algorithm operates quickly, but has a lower detection accuracy, whereas the 'two-stage' algorithm exhibits a slower running efficiency but a higher detection accuracy. These deep learning models can be deployed accordingly, depending on the specific application scenarios. For instance, in sudden situations requiring fast real-time detection, lightweight deployment using a 'two-stage' algorithm such as YOLO series models can be employed. To propose a suitable deployment scheme for the UAV inspection system, this study utilized prominent deep learning algorithms, namely Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s, for pavement crack object detection and a comparative analysis. The results revealed that Faster-RCNN demonstrated the best overall performance, with a precision (\(\mathbf{P}\)) of 75.6%, a recall (\(\mathbf{R}\)) of 76.4%, an \(\mathbf{F1}\)-score of 75.3%, and a mean Average Precision (\(\mathbf{mAP}\)) of 79.3%. Moreover, the mAP of Faster-RCNN surpassed that of YOLOv5s, YOLOv7-tiny, and YOLOv8s by 4.7%, 10%, and 4%, respectively. This indicates that Faster-RCNN outperformed in terms of detection accuracy, but required a higher environment configuration, making it suitable for online data collection using a UAV and offline inspection at work stations. On the other hand, the YOLO serial models, while slightly less accurate, were the fastest algorithms and are suitable for the lightweight deployment of UAVs with online collection and real-time inspection. Many studies have also proposed refined YOLO-based algorithms for crack detection in drones, mainly due to their lightweight deployment in UAV systems. For instance, the BL-YOLOv8 model [20] reduces both the number of parameters and computational complexity compared to the original YOLOv8 model and other YOLO serial models.