We propose an approach for traffic sign detection based on Convolutional Neural Networks (CNN). We first transform the original image into the gray scale image by using support vector machines, then use convolutional neural networks with fixed and learnable layers for detection and recognition. The fixed layer can reduce the amount of interest areas to detect, and crop the boundaries very close to the borders of traffic signs. The learnable layers can increase the accuracy of detection significantly. Besides, we use bootstrap methods to improve the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained competitive results, with an area under the precision-recall curve(AUC) of 99.73% in the category “Danger”, and an AUC of 97.62% in the category “Mandatory”.