A small-target traffic sign detection algorithm based on multiscale feature fusion is proposed to address the limited effectiveness of the existing target detection algorithms in detecting traffic signs with small sizes or inconspicuous features. First, a bidirectional adaptive-feature pyramid network is designed to extract all detail features and jump connections to enhance multiscale feature fusion. Second, a dual-head detection structure is proposed for the scale characteristics of small targets, focusing on small-target feature information while reducing the number of model parameters. Next, using the Wise-IoU v3 bounding box loss function and a dynamic nonmonotonic focusing mechanism, the harmful gradients generated by low-quality examples are reduced by employing the anchor-box gradient gain allocation strategy. Finally, coordinate convolution (CoordConv) is incorporated into the feature extraction network to enhance the spatial awareness of the model by improving the network's focus on coordinate information. The experimental results on the Tsinghua-Tencent 100K dataset show that the improved model has a mean average precision (mAP) of 87.7%, which is a 2.2 percentage points improvement over YOLOv5s. Moreover, the number of parameters is only 6.3×10⁷, thereby achieving a detection effect with fewer parameters and higher accuracy.