In the field of traffic sign detection， challenges arise due to the small area coverage of distant traffic signs in the scene and the diverse scales of the signs. To overcome the above challenges， this paper presents an improved YOLOv8s-based traffic sign detection algorithm， YOLOv8s-REMN. First， the method introduces the receptive field attention convolution （RFAConv） into the backbone network to enhance the receptive field and feature extraction capability of the network. Second， the efficient attention-guided feature module（EAGFM） module is added to the neck network to optimize multi-scale feature fusion. Then， the multi-scale detail enhancement fusion （MSDEF） module is incorporated into the detection head to increase the small object detection head， improving the detection of small targets. Finally， the normalized Wasserstein distance （NWD） loss function replaces the CIoU loss function to optimize the bounding box regression and improve the precision of small object localization. Experimental results show that YOLOv8s-REMN achieves significant performance improvements on the TT100K dataset. Compared to the original YOLOv8s， mAP@0.5 increases by 6.6% and mAP@0.5：0.95 increases by 5.1%. The effectiveness of the algorithm is also validated on the Chinese Traffic Sign Detection dataset， CCTSDB2021， where YOLOv8s-REMN outperforms YOLOv8s with a 2.9% increase in mAP@0.5 and a 2.9% increase in mAP@0.5：0.95.