As　one　of　the　important　parts　of　road　infrastructure,　traffic　signs　provide　vital　information　for　road　users.　Achieving　efficient　traffic　signs　retrieval　greatly　contributes　to　the　intelligent　analysis　on　big　traffic　data.　In　this　paper,　we　propose　a　saliency　guided　shallow　convolutional　neural　network　(CNN)　for　traffic　signs　accurate　and　fast　retrieval.　Firstly,　by　unifying　deep　saliency　and　hashing　learning　in　a　single　architecture,　the　proposed　CNN　model　performs　joint　learning　in　a　point-wise　manner,　which　is　scalable　on　large-scale　datasets.　Then,　deep　saliency　features　and　hashing-like　outputs　are　extracted　from　traffic　sign　images　with　the　saliency　guided　shallow　CNN.　The　binarized　hashing-like　outputs　together　with　saliency　features　are　used　to　construct　features　database.　Finally,　a　coarse　to　fine　similarity　measurement　is　performed　by　Euclidean　distance　and　Hamming　distance　to　return　retrieval　results.　Experimental　results　demonstrate　the　retrieval　accuracy　of　our　method　outperforms　five　state-of-the-art　methods　on　GTSRB　dataset.