针对当前道路损伤检测技术中存在的效率不高、成本过高以及不易于在移动设备上部署等挑战，提出一种基于改进YOLOv8算法的新型道路裂缝检测方法，命名为YOLOv8-RC（YOLOv8-road crack）。所提方法在YOLOv8n的架构基础上，对C2f模块进行改良，引入动态蛇形卷积技术以更精确地识别管状结构特征，同时能够自适应地关注于纤细和弯曲的局部结构。所提算法中新增一种效率高的多尺度注意力机制（efficient multi-scale attention, EMA），有效提升了识别精度。在模型的颈部结构中，加入加权双向金字塔网络（bi-directional feature pyramid network, BiFPN），实现了特征的多尺度融合，优化了算法的精度和效率。在RDD2022_China_MotorBike和RDD2022_Japan两个数据集上的实验结果显示，改进后的YOLOv8n-RC模型的mAP50分别为78.8%和43.8%，较原YOLOv8n模型分别提高了3.9%和3%。所提算法的模型参数总量仅为2.84 M，计算复杂度为7.8 G，从而证明了所提方法的实用性和优越性。

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.