## 5 Conclusions  
Automated pavement crack detection is a significant step which offers a low cost, is less labor-intensive, and represents a practical solution to replace traditional manual methods. In our study on crack detection, we have identified issues with existing works such as inappropriate datasets and an inadequate detection capability for small-sized cracks. In this paper, an intelligent model was implemented based on the improved YOLOv5 network to address these issues and detect pavement cracks with complex backgrounds automatically and accurately.  
Specifically, a dataset of pavement cracks with complicated backgrounds was built which is more comparable to engineering applications. Samples in the dataset were collected from real scenarios and divided into four classes, containing the overall development trend of cracks. For the network architecture, the YOLOv5 framework was improved by adding a small-target detection layer, updating the anchor box, improving the neck structure based on Bi-FPN, and improving the cross-layer connection, which effectively enhanced the network's understanding of features and detection capability of small targets. Experimental analyses revealed that the proposed crack detection model obtained a high performance for the AP of transverse, longitudinal, block, and alligator cracks with 81.75%, 83.81%, 98.20%, and 92.83% respectively, and the mAP for all cracks was 89.15%. Our proposed model also displayed an advantage in small-target detection, achieving more than a 5% improvement in the AP of transverse cracks and longitudinal cracks. From the perspective of engineering applications, more cracks were successfully detected by the proposed model, and the missed detection rate of the test set was reduced from 8.95% to 2.20%. In conclusion, both in terms of algorithm and engineering evaluation indicators, this methodology has significantly improved the crack detection capability and the model practicality of the original network.  
However, our model also has disadvantages and limitations. Since we focused mainly on small-target detection, the AP of block cracks decreased by 0.18% in contrast to the original YOLOv5 network. And when several cracks of multiple categories appear in the same area, the bounding boxes of block and alligator cracks may not describe the shape of the cracks so accurately. Additional feature fusion methods will be combined in future studies to enhance the feature extraction capabilities and broaden the model's applicability to multi-scale cracks. According to the crack detection results, our model can roughly evaluate the severity levels of cracks but cannot provide precise measurement data of cracks. The segmentation of pavement cracks could be investigated in subsequent research to obtain the width information of cracks which is more important for classifying severity levels. Additionally, the number of cracks in the dataset needs to be further expanded to cover more scenarios in the future.  
Conceptualization, Y.W. and Q.Q.; methodology, Y.W.; software, Y.W. and L.S.; validation, Y.W., W.X., C.L. and T.M.; formal analysis, Y.W. and J.Z.; investigation, C.L. and L.S.; resources, Q.Q.; data curation, W.X., C.L. and T.M.; writing--original draft preparation, Y.W. and J.Z.; writing--review and editing, C.L. and T.M.; visualization, Y.W., W.X. and T.M.; supervision, Q.Q., L.S. and W.X.; project administration, Y.W. and Q.Q.; funding acquisition, Q.Q. and W.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Innovation and Entrepreneurship Science and Technology Project of Chinese Institute of Coal Science, Grant/Award Number: 2021-KYJ-005 and the Innovation and Entrepreneurship Science and Technology Project of Chinese Institute of Coal Science, Grant/Award Number: 2022-TD-MS001.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy and ethical restrictions.  
The authors declare no conflicts of interest.