In response to the challenges of low efficiency, high cost, and difficulty in deployment on mobile devices in current road damage detection technology, a novel road crack detection method based on the improved YOLOv8 algorithm, named YOLOv8 road crack (YOLOv8-RC), was proposed. The C2f module, based on the YOLOv8n architecture, was enhanced through the introduction of dynamic snake convolution technology, which more accurately identified tubular structural features and adaptively focuses on fine and curved local structures. Furthermore, a highly efficient multi-scale attention(EMA) mechanism was incorporated into the algorithm, effectively enhancing recognition accuracy. In the neck structure of the model, a weighted bidirectional pyramid network(BiFPN) was added to achieve multi-scale fusion of features, thereby optimizing both the accuracy and efficiency of the algorithm. Experimental results on the RDD2022-China-MotorBike and RDD2022-Japan datasets demonstrate that the improved YOLOv8n-RC model achieves mAP50 scores of 78.8% and 43.8%, respectively, representing improvements of 3.9% and 3% over the original YOLOv8n model. The total number of model parameters for the proposed algorithm is only 2.84 M, and the computational complexity is 7.8 G, underscoring the practicality and superiority of this method.