A novel classification method is presented for recognizing traffic sign symbols undergoing image degradations.In order to cope with degradations

the classifier uses the combined blur-affine invariants（CBAIs） of traffic sign symbols as the feature vectors which allow to recognize objects in the degraded scene without any restoration.A radial basis probabilistic neural network（RBPNN） is designed with recursive orthogonal least algorithm（ROLSA） and applied to the classification of degraded traffic signs.The simulation results indicate that CBAIs are efficient to the feature extraction of degraded images and the classification and generalization performance of the RBPNN classifier with the reduced structure are good.