## 1 Introduction
[25] proposed an improved YOLOv5 algorithm based on transformer heads and the self-attention mechanism, which effectively improved the detection and classification capabilities of concrete cracks, with a mean accuracy (mAP) of up to 99.5%. [PERSON] et al. [26] presented a lightweight LCANet backbone and a novel efficient prototype mask branch for crack detection based on the YOLOv8 instance segmentation model, reducing model complexity. Specifically, under conditions of 129 frames per second (FPS), the results of the case study showed the accuracy reached 94.5%, while the computational complexity decreased by 51%, compared to the original model. The improvements made to the YOLO algorithm focus on optimizing the model structure, improving the loss functions and the feature extractors, and optimizing the data pre- and post-processing methods. These improvements have enhanced detection accuracy, speed, and robustness. However, in engineering applications, the underside of concrete bridge structures, where significant forces are applied, is prone to cracking, but these areas often have inadequate lighting conditions. In addition, structural corners and edges are susceptible to defects such as honeycombing and exposed rebar due to casting problems, but these areas have complex backgrounds and weak surface textures. In such cases, the YOLO algorithm can suffer from missed detections and false positives. To address these challenges, the deformable convolutional network attention YOLO (DCNA-YOLO) algorithm based on YOLOv8s is proposed in this study. A multi-branch coordinate attention mechanism (MBCA) is introduced to simultaneously incorporate spatial position information and global information. The attention weights for direction perception, position sensitivity, and global awareness are optimized to comprehensively improve the accuracy of coordinate localization. This effectively highlights features of target defect areas with uneven reflections and weak textures, thereby improving the representation and detection effects of the target detection algorithm. Thus, the balance between detection performance, speed, and model parameter size is achieved using MBCA. Furthermore, a deformable convolution method, named MBCADC, based on MBCA is presented. By embedding MBCA attention, this method improves the adaptability of the deformable convolution to significant illumination changes and complex feature shapes in regions with uneven reflections. As a result, it better accommodates different image structures and texture features. The complexity of the model is reduced, while recall (R) and average precision are improved, and missed detections and false positives are reduced. The paper is organized as follows. Firstly, an overview of the YOLO algorithm is given for the problem under study, and the improvements of existing methods are compared. Subsequently, an improved framework that incorporates deformable convolution (DC) modules with the multi-branch coordinate attention (MBCA) mechanism is introduced. Finally, this new framework is validated with a dataset containing 4794 damage images and compared with other algorithms. Flowchart of study as shown in Figure 1.