In order to solve the problem of low detection accuracy of traffic signs due to small targets in real traffic scenes, a traffic sign detection algorithm based on improved SSD was proposed. First, a deeper ResNest network was used to replace VGG16, the backbone network of the original SSD algorithm, to enhance the strong characterization of weak target features. Then, the RFB module was used in the additional layer of SSD to increase the receptive field of small targets. Secondly, bi-FPN weighted bidirectional feature pyramid network is used to effectively combine deep and shallow feature information to improve the detection performance of small targets. Finally, K-means++ clustering algorithm was used to adjust the size of the default window, which effectively avoided the problem that the original default window was too large but the traffic signs were small and could not be matched, so as to improve the detection efficiency. Experimental results show that the proposed model achieves 95.33% mAP on the China Traffic Signs Data Set (CCTSDB). Compared with the original SSD model, the proposed model can better adapt to traffic signs detection under natural background.