## 5 Conclusions  
The current work adopts a novel object detection algorithm termed DCNA-YOLO based on multi-channel attention mechanisms and deformable convolutions. It is proposed to address problems such as missed detections, false positives in regions with insufficient illumination, complex backgrounds, and weak surface textures on concrete bridge surfaces. The major findings of this work are concluded below:  
(1) Multi-branch coordinate attention (MBCA) is adopted on the basis of CA with a supplementation of the global information branch. MBCA is applied to obtain spatial coordinate information and global information simultaneously, which improves the accuracy of coordinate information in the attention mechanism.  
(2) The MBCA mechanism is embedded with a deformable convolution, then used to enhance the adaptability of the convolution kernel. This novel coordinated model contributes the coordinate localization ability for better adaptation to different image structures and texture features.  
(3) The proposed framework (Figure 8) is validated through a self-constructed concrete surface defect dataset. Our results effectively highlight the accuracy of detecting regions with significant light variations, uneven reflections, and weak textures without increasing the complexity. All of these mitigate the problems of missed detections and false alarms.  
(4) The next plan is to prune and distill the knowledge of the DCNA-YOLO model to develop a lighter model that can be deployed on resource-constrained concrete bridge health inspection drones for efficient real-time inspection. This will improve the safety, efficiency, and accuracy of the inspection and provide a scientific basis for the maintenance and management of concrete bridges.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Parameters/M** & **FLOPs/G** & **FPS/f\(\cdot\)s\({}^{-1}\)** & **P/\%** & **R/\%** & **mAP\({}_{0.5}\)/\%** & **mAP\({}_{0.5}\)/\%** \\ \hline YOLOv3-tiny & 12.1 & 19.1 & 76.9 & 81.7 & 73.4 & 78.6 & 53.4 \\ YOLOv5s & 7.0 & 16.8 & 78.3 & 91.2 & 84.9 & 88.7 & 67.4 \\ YOLOv6s & 16.3 & 44.2 & 69.4 & 90.2 & 81.5 & 87.7 & 69.6 \\ YOLOv8s & 11.1 & 28.7 & 70.9 & 89.1 & 82.0 & 87.4 & 68.9 \\ Proposed method & 11.3 & 27.5 & 74.4 & 91.3 & 85.4 & 89.4 & 73.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results of different network models.  
**Author Contributions:** Conceptualization, T.L.; methodology, T.L.; software, T.L.; validation, T.L. and S.T.; formal analysis, G.L.; investigation, G.L.; resources, G.L.; data curation, T.L.; writing--original draft preparation, T.L.; writing--review and editing, S.T.; visualization, T.L.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (52221002, 52078084), Fundamental Research Funds for the Central Universities (2023 CDJKYJH093), and the 111 project of the Ministry of Education and the Bureau of Foreign Experts of China (Grant No. B18062).  
**Data Availability Statement:** The data used in this paper can be obtained through the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.