Objective: The structural integrity of bridges is a critical concern as infrastructure ages, necessitating the development of reliable methods for detecting potential failures. Among these, the identification of small target cracks is particularly important, as these cracks often grow undetected until they result in severe damage. Traditional inspection methods, such as manual visual inspections, are hindered by their labor-intensive nature and susceptibility to human error, often resulting in the oversight of small but significant defects. Recent advancements in computer vision and deep learning technologies offer new opportunities to improve the accuracy and efficiency of bridge inspections. This study introduces an innovative approach for detecting small target cracks in bridge structures by employing an enhanced version of the You Only Look Once (YOLOv8) object detection model, a widely recognized algorithm known for its rapid processing capabilities and high detection accuracy. The enhanced YOLOv8 model is tailored to detect small-scale cracks on bridge surfaces that may not be easily identifiable by traditional inspection methods or earlier versions of computer vision models. Methods: The proposed algorithm modifies the standard YOLOv8 model to address the specific challenges associated with detecting small cracks on bridge surfaces. A key modification is the integration of efficient vision transformer (EfficientViT) into the backbone of the YOLOv8 model. EfficientViT is an advanced transformer-based architecture that reduces redundant parameters and optimizes the extraction of local features from high-resolution images, enabling more precise detection of subtle crack features. This enhancement is crucial, as small cracks often exhibit low contrast against their background and may be easily overlooked by less sophisticated models. In addition to EfficientViT, the proposed algorithm also incorporates large selective kernel network (LSKNet) within the C2f module of YOLOv8. LSKNet employs a dynamic kernel selection mechanism that allows the model to adaptively adjust the size of the convolutional kernels based on the input features, making it highly suitable for detecting cracks of varying sizes, orientations, and morphological characteristics. This adaptability ensures that the model can detect small cracks, regardless of their form. Furthermore, the model uses bidirectional feature pyramid network (BiFPN) to merge feature maps at different scales. Traditional models struggle with detecting small targets due to the loss of critical information during downsampling operations. BiFPN mitigates this issue by preserving high-resolution feature maps across multiple layers, enhancing the model's ability to detect small cracks that would otherwise be missed. The combined effect of these modifications improves the accuracy of small target crack detection while maintaining computational efficiency. Results: The effectiveness of the proposed model was validated using a dataset of crack images from a specific bridge, captured by unmanned aerial vehicles (UAVs). UAVs provided detailed images from areas that were often difficult or dangerous to access using traditional inspection methods. The experimental results demonstrated that the enhanced YOLOv8 model significantly outperformed the original version in terms of key performance metrics. Specifically, the modified model achieved improvements of 3.7%, 3.5%, 3.5%, 3.9%, and 7.4% in terms of the detection precision, recall, F₁ score, mAP50, and mAP50-95, respectively. These results indicated a substantial improvement in the model's ability to detect small cracks that often had low contrast and irregular shapes, which were typical characteristics of cracks on bridge surfaces. Furthermore, compared to conventional methods, the proposed model was able to detect cracks with higher precision and fewer false positives, making it a promising tool for improving the efficiency of bridge inspections. Conclusions: In conclusion, the improved YOLOv8 algorithm introduced in this study represents a significant advancement in the detection of small target cracks in bridge structures. The modifications made to the original YOLOv8 model, including the integration of EfficientViT, LSKNet, and BiFPN, result in a more accurate and computationally efficient model for crack detection. This approach offers a practical and scalable solution for the widespread application of bridge health monitoring, particularly in areas that are difficult to inspect using traditional methods. By leveraging advanced surface data processing techniques, this research contributes to the development of modern methods for assessing the health of bridge structures, ultimately helping to ensure the safety and longevity of infrastructure systems.