To tackle the current challenges of low efficiency, subpar performance, and inadequate real-time capabilities in bridge crack detection, this paper introduces an improved YOLOv8 model-based drone image detection method for bridge cracks. Firstly, the dynamic snake convolution kernel is integrated into the C2f module in the backbone of YOLOv8 to enhance the crack feature extraction ability. Then, the CAM module was introduced to improve the ability of small target detection. Finally, the influence of low-quality data sets on detection results was reduced by optimizing the prediction box loss function. The experimental results show that the GFOLPs of the improved model reaches 14.4 and mAP@50 reaches 94%. Compared with the basic model, the accuracy of the improved model is greatly improved, and the detection speed reaches 147 frames /s, which can meet the requirements of real-time crack detection by UAVs.