Due to the low accuracy and false detection of small target detection algorithm in traffic sign detection , a new foreground fusion attention mechanism network called YOLO-Traffic is proposed. First , EIOU loss function is introduced to calculate the width of the predicted frame and the real frame respectively , and the dilated convolution is used to solve the problems existing in the original CIOU model. Secondly , the foreground attention mechanism F-ECA was added to fully extract the foreground information and suppress background noise. Finally , Kmeans+ + algorithm is used to replace the anchor frame obtained by Kmeans clustering to reallocate the corresponding feature layer and further improve the feature extraction ability. The experiment on TT100K traffic sign data set produced by Tsinghua University shows that compared with the original YOLOv5 network and the accuracy is increased by 2. 91% , the recall rate is in- creased by 2. 1% , the detection speed is 44 frames per second , and the final accuracy reaches 96. 89% . Hence , the proposed YOLO-Traffic network promotes the accuracy of traffic sign detection and model performance.