Speed-limit Sign Recognition Using Convolutional Neural Network Based on Random Forest

= >?@ABCD>!.AEFG,HI!J >KLMGNOPQR>S:.TG /0 .1 234UVWX+ Y.Z. /0 .1 234[\]?^_`a34b>?cd.ef[gh6:i@jCNN (Convolutional neural network)[k-.Z. l mn!CNN5opqr[\]Zs!5tu,k-v:qrwxy>N]MLP(Multi-layer perceptron) Ry!z{|x(fully-connected) 4 [k-}~€ yU‚,ƒ,„ @†‡,iZ. = >?@,ˆ.†‡[‰,\]2Xu!CNN[Šel:`a+\](Random forest)+x‹lhz{|x,Œ|x4 [Ž-lZ. GTSRB(German Traffic Sign Recognition Benchmark),‘!’“Y{1 2”>?8•/0 .1 2,‘+k-lh Yl@4 ,SVM (Support Vector Machine),HMLP +Ž-–—gZef,˜s}[™šlZ. AbstractIn this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).Keyword : Convolutional Neural Network, Random forest, speed-limit sign recognition , feature extraction, ADAS