In order to solve the problem that the real time performance is not hi gh since processing needs to be carried out in each detected sub-window when traditional methods based on machine learning are used in traffic sign detection,a rapid traffic sign detection (TSD) algorithm based on region of interest (ROI) extraction and machine learning is proposed in this paper.Aiming at the shortcomings that traditional ROI extraction method based on color threshold has more sensitive to illumination changes,we design an effective method based on maxima lly stable extremal regions (MSER) under color enhancement.In this method,image is firstly enhanced by the color characteristics of traffic signs,and the ROI of traffic signs is obtained by extracting MSERs from co lor enhanced image.Then in the process of multi-scale sliding and traversing window detection,histogram of oriented g radients (HOG) feature is extracted only in the sliding window which contains the ROI of traffic sig ns.And finally,classification and discrimination are completed through the support vector machine (SVM).Experimental results show that the computing speed of presented approach in this paper has improved greatly.It also has good robustness,and can achieve accuracy 96.42% of and a low false alarm number.