## 8 Conclusions  
The traditional manual inspection of road cracks is inefficient, time-consuming, and labor-intensive. Additionally, using multifunctional road inspection vehicles can be expensive. However, the use of UAVs equipped with high-resolution vision sensors offersa solution. These UAVs can remotely capture and display images of the pavement from high altitudes, allowing for the identification of local damages such as cracks. The UAV inspection system, which is based on the commercial DJI Min2 drone, offers several advantages. It is cost-effective, non-contact, highly precise, and enables remote visualization. As a result, it is particularly well-suited for remote pavement detection. In addition, automatic crack detection technology based on deep learning models brings significant additional value to the field of road maintenance and safety. It can be integrated into the commercial UAV system, thereby reducing the workload of maintenance personnel. In this study, the contributions are summarized as follows: (1) A pavement crack detection and evaluation framework of a UAV inspection system based on deep learning was proposed and can provide technical guidelines for road authorities. (2) To enhance automatic crack detection capability and design a suitable scheme for implementing deep-learning-based models in a UAV inspection system, we conducted a validation and comparative study on prevalent deep learning algorithms for detecting pavement cracks in urban road scenarios. The study demonstrates the robustness of these algorithms in terms of their performance and accuracy, as well as their effectiveness in handling our customized crack image datasets and other popular crack datasets. Furthermore, this research provides recommendations for leveraging UAVs in deploying these algorithms. (3) Quantitative methods for road cracks were proposed and pavement distress evaluations were also carried out in our experiment. Obviously, our final evaluation results were also guaranteed according to _GSD_. (4) A pavement crack image dataset integrated with _GSD_ was established and has been made publicly available for the research community, serving as a valuable supplement to existing crack databases. In summary, the UAV inspection system, under the guidance of our proposed framework, has been proven to be feasible, yielding more satisfactory results. However, drone inspection has the inherent limitation of a limited battery life, making it difficult to perform long-distance continuous road inspection tasks. Drones are better suited for short-distance inspections in complex urban scenarios [16]. With advancements in drone and vision computer technology, drones equipped with lightweight sensors and these lightweight crack detection algorithms are expected to gain popularity for road distress inspection. In the future, this study aims to incorporate improved YOLO algorithms into the UAV inspection system to enhance road crack recognition accuracy. Furthermore, in order to establish a comprehensive UAV inspection system for road distress, we plan to continue researching multi-category defect detection systems in the future, including various road issues such as rutting and potholes, among which are cracks. Additionally, efforts will be made to enhance UAV flight autonomy for stability and high-speed aerial photography, further improving the quality of aerial images and catering to the requirements of various complex road scenarios. Conceptualization, X.C., C.L. and L.C. ; methodology, X.C. and L.C. ; software, C.L. and L.C. ; validation, C.L., X.Z. and L.C. ; formal analysis, X.C., X.Z. and Y.Z. ; investigation, L.C., C.L., C.W. and Y.Z. ; resources, X.C. and C.L. ; data curation, C.L. and C.W. ; writing--original draft preparation, X.C., C.L. and Y.Z. ; writing--review and editing, X.C. All authors have read and agreed to the published version of the manuscript. This research was funded by China Postdoctoral Science Foundation (2017M622577), Hunan Provincial Natural Science Foundation (2018J2118).