Traffic sign detection is a key technology in driving assistance syste ms.The effective detection for prohibitive signs is crucial to ensure the driver safety and reduce the traffic accidence.In this paper,we propose an adaptive detection method for prohibitive signs based on the radial symmetry transform.A traffic image is segmented based on RGB normalized threshold algorithm and a binary image is gene rated.Then a regional contour feature vector is constructed and used to determine and extract every ca ndidate sign region.For the region with possible mutually occluding signs,an adaptive separation algorithm based on the watershed transform is presented to perform signs separation.For each abstracted cand idate sign region,the key parameters in the radial symmetry transform are set adaptively according to the scale feature of the region,and an adaptive radial symmetry circular detector and corresponding detection approa ch are constructed and used to detect the prohibitive signs and reject the non-sign regions.The performance of the propo sed method is tested on the German traffic sign detection benchmark (GTSDB).Experimental results demonstrate the effectiveness of our method.Our method outperforms the existing algorithms in terms of execution effi ciency,detection rate and algorithm complexity,and ours has the robustness to th e object scale variation,variable lighting condition and weather,motion blurring and mutual occlusion of signs,etc.