This　paper　proposes　an　effective　method　for　traffic　sign　detection　by　employing　deep　random　mapping　autoencoder　network.　The　architecture　is　composed　of　three　modules:　coarse　detection,　fine　detection,　and　candidates　clustering.　The　method　utilizes　histogram　of　oriented　gradient　and　color　histogram　to　express　the　features　of　traffic　signs.　Our　method　is　simple　and　extensible.　Results　are　indicated　on　both　German　traffic　sign　detection　benchmark　and　Belgium　traffic　sign　detection　dataset.　Our　method　achieves　99.27%　area　under　the　precision-recall　curve　(AUC)　for　all　categories　of　traffic　signs　on　German　traffic　sign　detection　benchmark,　and　93.34%　AUC　for　all　categories　on　Belgium　traffic　sign　detection　dataset.　©　2018　IEEE.