An improved YOLOv8 algorithm is proposed to address the problems of low detection accuracy and weak generalization ability in existing roadbed slope crack detection algorithms. Firstly, a reparameterization module is embedded in the backbone network to lighten the model while capturing crack details and global information, improving detection accuracy of the model. Secondly, the C2f-GD module is designed to achieve efficient fusion of model features and enhance the generalization ability of the model. Finally, the lightweight detection head L-GNHead is designed to improve the crack detection accuracy for different scales, while the SIoU loss function is used to accelerate model convergence. The experimental results on the self-constructed roadbed slope crack dataset show that the improved algorithm improves mAP50 and mAP50-95 by 3.3% and 2.5% respectively, reduces parameters and computational costs by 46.6% and 44.4% respectively, and improves FPS by 18 frames/s compared with the original algorithm. The generalization validation results on the dataset RDD2022 show that the improved algorithm not only achieves higher detection accuracy, but also faster detection speed.