A lightweight traffic sign detection network incorporating foreground attention, YOLOT, is proposed to address the problem that object detection algorithm models are prone to error and miss detection on traffic sign detection. Firstly, the introduction of the SiLU activation function to improve the accuracy of model detection; secondly, a lightweight backbone network based on the ghost module is designed to effectively extract object features; thirdly, introduction of foreground attention perception module to suppress background noise; fourthly, we improve the path aggregation network by adding a residual structure to the feature fusion process; finally, we use VariFocalLoss and GIoU to calculate the classification loss of objects and the similarity between objects. Extensive experiments are conducted on several datasets, and the results show that the accuracy of the method in this paper is better than the current state-of-the-art methods. Ablation experiments are conducted on the CCTSDB dataset, and the final accuracy reaches 98. 50%, with an accuracy improvement of 1. 32% compared to the baseline model, while the model is only 4. 7 MB, and the real-time detection frame rate reaches 44 frames per second.