Detecting traffic sign small targets accurately and in real time in natural environment is of great significance for automatic driving and intelligent transportation, however, the existing algorithms are difficult to balance the speed and accuracy. Based on the YOLOv7-tiny algorithm, a real-time traffic sign small target detection algorithm that improves YOLOv7-tiny was proposed, namely YOLO-T algorithm. CondConv (conditional parameterized convolution) structure was used to enhance the feature extraction capability of the backbone network. To enhance the localization accuracy of small targets and ensure the detection speed, the TinyFPN feature fusion network structure and ELAN-P network aggregation layer were designed. To verify the effectiveness of the YOLO-T algorithm, ablation experiments and comparison experiments were done on the TT100K dataset. The experimental results show that YOLO-T improves mAP (mean average precision) by 16.8% over the YOLOv7-tiny algorithm with the same training samples and training device parameters, and the detection time of a single image is only 10.2 ms. Therefore, the proposed YOLO-T algorithm is able to balance the speed and accuracy of the detection of the small targets of traffic signs.