To address the problems of detection accuracy degradation in small traffic sign targets and excessive model complexity in complex scenes, this study proposes a lightweight traffic sign detection method using an improved YOLOv8n. An omni-dimensional dynamic convolution and efficient multi-scale attention (EMA) mechanism are introduced into the backbone network of YOLOv8n to accurately acquire sign features and context information. A small target detection layer of 160 pixel×160 pixel is added to effectively combine features with different scales, preserve more detailed information, and improve the precision of small target detection. GhostBottleneckv2 is introduced for lightweight processing, and the GSConv module is designed to reduce model complexity and accelerate convergence speed. The WIoU v3 loss function is used to enhance the ability of the model to locate the targets. The experimental results show that the proposed algorithm improves the mean average precision by 7.6 percentage points and 2.4 percentage points and decreases the parameter number by 7.6% and 7.9% on the TT100K and CCTSDB2021 datasets, respectively. Hence, the proposed algorithm not only maintains the lightness characteristics of the YOLOv8n model, but also exhibits better detection performance.