Automatic　identification　for　traffic　signs　is　an　important　part　of　intelligent　driving　and　traffic　safety.　Deep　learning　has　already　made　a　great　achievement　in　traffic　sign　detection.　However,　the　camera　on　a　car　may　capture　a　low　resolution　and　blurry　image　in　certain　environments,　which　make　it　inaccurate　for　traffic　sign　detection.　Therefore,　we　propose　a　method　based　on　image　super-resolution　reconstruction　for　improving　the　detection　rate　of　traffic　signs.　Firstly,　a　low-resolution　image　is　transformed　by　CNN-based　super-resolution　network　into　a　high-resolution　one.　Then,　to　meet　the　requirements　of　on-line　processing,　we　use　the　generated　super-resolution　image　as　input　for　the　detection　network　with　16　filters　in　this　layer.　At　last,　we　separately　trained　two　CNNs　for　super-resolution　reconstruction　and　traffic　sign　detection,　which　reduce　the　processing　time.　Experimental　results　demonstrate　that　our　model　can　achieve　better　performance　than　the　existing　methods　for　traffic　sign　detection.　©　2019　IEEE.