In order to address the issues of missed detections, false positives, and low accuracy in small traffic sign detection, this paper proposes a detection model for small traffic signs, named YOLOv8-Faster-Ghost-GAM. The algorithm introduces a global attention mechanism (GAM) into the last C2f module of the backbone network, enhancing key features and suppressing irrelevant information to significantly improve the detection of small targets and the recognition capability in complex scenarios. Additionally, each C2f module in the backbone network is replaced with FasterNet to reduce the number of model parameters, and standard convolutions are replaced with Ghost convolutions, which use inexpensive linear transformations to reduce computational effort. Finally, the WiOU loss function is employed to effectively improve the recognition of low-quality samples, resulting in a 1.6% increase in precision and a 3.2% increase in recall, thereby demonstrating the effectiveness of the proposed improvements.