Single Shot MultiBox Detector (SSD) is generally considered to be suitable for solving small target detection in images. However，its performance on feature extraction and detection efficiency is still required to be improved. A clustering residual SSD model is proposed in this paper. On one hand，in order to improve the feature extraction quality，the basic network VGG16 which consists of the original SSD model is replaced with a deeper residual network ResNet50. On the other hand, in order to improve the detection efficiency, K-means algorithm other than the blind search mechanism used in the original SSD model is exploited to find and determine the assignments of the sizes of default windows. For German traffic sign detection dataset, it obtains 97.1% mAP in detection accuracy and 0.07 s per image in detection efficiency. For Chinese traffic sign dataset, it obtains 89.7% mAP in detection accuracy and 0.08 s per image in detection efficiency. Compared with the original SSD model, the proposed model obtains the improved detection performance.