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Traffic sign detection has important applications in the field of unmanned driving. Aiming at the problem of low detection accuracy of traffic signs in complex environments, this paper optimizes and improves the SSD algorithm, and proposes to replace the feature extraction network from VGG16 with Resnet50 with stronger feature extraction capabilities. In order to improve the detection effect, this paper uses the K-means++ clustering algorithm Determine the size of the a priori box in the SSD. Based on the TensorFlow deep learning framework, this paper locates and classifies traffic signs in images in complex environments, and uses the SSD model and the improved SSD model to perform detection and comparison experiments, and analyze the model test results. The results show that the improved method has higher detection accuracy for various types of traffic signs than the original SSD algorithm. The method in this paper can achieve better results in detecting traffic signs.