## 5 Conclusions  
A deep learning model is applied to the UAV road crack-detecting scenario in this work. The GC-YOLOv5s UAV crack-detection model is offered as a tiny and easily deployable UAV crack-detection model. Based on the original YOLOv5s, the model improves the loss function and backbone network and proposes two new modules, C3 Ghost and CSPCM, to replace the original C3 module to improve the detection effect of the network and reduce the number of network parameters and model volume, making it more suitable for UAV detection scenarios. The UMSC dataset of road cracks photographed by UAVs is then developed, which covers three types of widely spread roads and includes the UAV detection issues of shadows, occlusions, low contrast, and huge levels of road noise. Finally, the validation is carried out based on this dataset in comparison with various types of algorithms. The results show that the GC-YOLOv5s model improves Precision by 8.2%, mAP_0.5 by 2.8%, mAP_0.5:0.95 by 3.1% and decreases the number of parameters by 16.2% compared with the pre-improvement. The GC-YOLOv5s model outperforms other models in Precision, Recall, mAP_0.5 and mAP_0.5:0.95, and the number of parameters decreases by 2.4% to 32% compared with other models of the same volume. Figure 7: Diagram of the Effectiveness of GC-YOLOv5s and other algorithms. The GC-YOLOv5s crack-detection network suggested in this study is novel in terms of improving the loss function, optimizing the network topology, and lightweight the module design, yielding more satisfactory results. This crack-detection technology provides significant additional value to the field of road maintenance and safety. It can be utilized in automated road maintenance and monitoring systems, greatly reducing the workload of maintenance personnel. The automatic detection and localization of road cracks contributes to the early identification and mitigation of potential road hazards, thereby reducing the risk of traffic accidents. Furthermore, it can be employed in planning road maintenance activities, enhancing the lifespan of road infrastructure, and saving maintenance costs. From a practical perspective, this technology offers substantial assistance to traffic management authorities, urban planners, and infrastructure maintenance companies. It enables them to respond more swiftly to road issues and take timely measures, thereby improving road availability and safety. With the broad and diverse development of UAV detection; however, tiny and more generalized models must still be researched. In order to establish a comprehensive UAV road defect detection system, we plan to continue researching multi-category defect detection systems in the future, including various road issues, among which are cracks. We will also make efforts to reduce the model's size to accommodate the expanding detection categories while enhancing computational efficiency. Additionally, we recognize that real-world applications may be influenced by varying road conditions and environmental factors, which could impact detection performance. Therefore, we will delve into optimizing and refining our model under diverse scenarios. Conceptualization, Y.Z. and Y.D. ; methodology, X.X. ; software, H.H. ; validation, H.H., S.W. and Y.D. ; formal analysis, X.X. ; investigation, H.H. ; resources, X.X. ; data curation, H.H. ; writing--original draft preparation, H.H. ; writing--review and editing, S.W. ; visualization, H.H. ; supervision, Y.Z. ; project administration, Y.D. ; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript. This research was funded by Open Foundation of the Key Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang Province (SZKF-2022-R04), Zhejiang University of Science and Technology 2022 postgraduate research innovation fund projects (2022 yjskc06), Zhejiang Provincial Natural Science Foundation (LY19F030004), Zhejiang Provincial Department of Transportation Science and Technology Plan Project (202206). Not applicable.