In order to reduce the interference of environmental factors such as complex weather, lighting changes, and traffic sign fouling, as well as the degradation of model performance caused by the high complexity of the algorithm itself, a highway road traffic sign detection algorithm based on RT-DETR as the benchmark model is proposed. Firstly, the designed lightweight module CSP-PMSFA is used as the backbone network of the algorithm to reduce the computational and parameter complexity of the model and improve its expressive power. Then, to address the issues of high computational complexity, small model capacity, and limited computational efficiency in the feature interaction module within the original algorithm scale, cascaded group attention CGA was introduced for improvement. Finally, a cross scale feature fusion module EMBSFPN was designed to address the issues of insufficient receptive field adaptability and limited feature information processing; the decoding mechanism using EUCB upsampling module preserves and fuses feature information while ensuring accuracy, optimizing performance and improving the robustness of the model. The experimental results show that the improved algorithm has improved mAP50 by 2.6% and FPS by 13.6 frames compared to the original algorithm on the ROAD MARK road marking detection dataset. The computational and parameter requirements have decreased by 29.7% and 39.6%, respectively. Overall, it outperforms other improved algorithms and improves detection accuracy and speed while being lightweight, demonstrating practicality.