## 1 Introduction  
Bridges, which are crucial to national economic development, play an important role in the transport infrastructure. The number of constructed and operational bridges has exceeded 1.2 million in China, of which more than 70% are concrete bridges. As the service life increases, concrete bridges are inevitably subjected to various superficial defects such as cracking, concrete spalling, and rebar corrosion due to the combined effects of material aging, external loads, and the working environment [1]. These defects can affect the load-bearing capacity, service life, and overall safety of bridge structures [2]. Due to the large number and scale of concrete bridges, traditional manual inspections are inefficient to meet the demands of routine inspections. In addition, structures such as high piers and long spans require expensive auxiliary equipment for close-up inspection, which further increases inspection costs. With the improvements in resolution and accuracy of cameras, there is growing interest in using computer vision pattern recognition, including machine learning and deep learning, [3] to automatically detect bridge defects [4; 5; 6; 7]. Machine learning methods mainly rely on template matching for defect detection, so such methods rely heavily on manual experience for sample feature extraction. In addition, single-layer features constructed by these methods have limited recognition ability when dealing with complex features and noise. Therefore, deep learning methods that can adaptively learn and extract image features have become mainstream, which are categorized into one-stage and two-stage methods based on the overall training conditions. One-stage detection methods extract features directly in the network to predict object classification and location, including the You Only Look Once (YOLO) [8] and Single Shot Multibox Detector (SSD) algorithms [9]. Two-stage methods typically use a RegionProposal Network (RPN) to extract potential target area which typically exhibit higher detection accuracy and stability as well as more computing resources, such as Regions with Convolutional Neural Network (RCNN) [10], Fast RCNN [11], Faster RCNN [12], and Mask RCNN [13]. The YOLO algorithm has been continuously improved since it was proposed by [PERSON] et al. in 2016, whose detection accuracy and computing speed have been further improved [8; 14; 15; 16; 17; 18; 19; 20; 21]. The main feature of the YOLO algorithm is that it transforms object detection into a regression problem, dividing the image into s \(\times\) s grids and directly predicting the class probabilities and position information within the corresponding bounding box of each grid. YOLOv8 [21] adopts an anchor-free approach, combines the Task-Aligned assigner positive sample assignment strategy [22] and decoupled head, and introduces a C2F structure with a richer gradient flow and distribution focal loss, further improving detection accuracy and speed. However, the YOLO algorithm was primarily designed for general image classification. To improve its effectiveness in identifying concrete cracks, [PERSON] et al. [23] proposed a YOLOv5 model enhanced with a fusion of spatial pyramid pooling cross-stage partial connections (SPPCSPCs) and a transposed convolution to detect cracks on bridge surfaces from different angles, demonstrating superior detection performance compared to other models on the ZJU SYG dataset (a crack data set for object detection based on deep learning provided by Zhejiang University). [PERSON] et al. [24] introduced a concrete structure crack detection method based on an improved YOLOv5, using a threshold segmentation method based on Otsu's maximum inter-class variance to remove background noise in images, and optimizing the initial anchor box sizes with the K-means method. The improved average accuracy in complex environments increased by 6.87%. [PERSON] et al.