This study proposes an algorithm for road damage detection based on an improved YOLOv8 to address challenges in road damage detection, including multi-scale targets, complex target structures, uneven sample distribution, and the impact of hard and easy samples on bounding box regression. The algorithm introduces dynamic snake convolution (DSConv) to replace some of the Conv modules in the original faster implementation of CSP bottleneck with 2 convolutions (C2f) module, aiming to adaptively focus on small and intricate local features, thereby enhancing the perception of geometric structures. By incorporating an efficient multi-scale attention (EMA) module before each detection head, the algorithm achieves cross-dimensional interaction and captures pixel-level relationships, improving its generalization capability for complex global features. Additionally, an extra small object detection layer is added to enhance the precision of small object detection. Finally, a strategy termed Flex-PIoUv2 is proposed, which alleviates sample distribution imbalance and anchor box inflation through linear interval mapping and size-adaptive penalty factors. Experimental results demonstrate that the improved model increases the F1 score, mAP50, and mAP50-95 on the RDD2022 dataset by 1.5%, 2.1%, and 1.2%, respectively. Additionally, results on the GRDDC2020 and China road damage datasets validate the strong generalization of the proposed algorithm.