In　this　paper,　a　vehicle　color　recognition　method　using　lightweight　convolutional　neural　network　(CNN)　is　proposed.　Firstly,　a　lightweight　CNN　network　architecture　is　specifically　designed　for　the　recognition　task,　which　contains　five　layers,　i.e.　three　convolutional　layers,　a　global　pooling　layer　and　a　fully　connected　layer.　Different　from　the　existing　CNN　based　methods　that　only　use　the　features　output　from　the　final　layer　for　recognition,　in　this　paper,　the　feature　maps　of　intermediate　convolutional　layers　are　all　applied　for　recognition　based　on　the　fact　that　these　convolutional　features　can　provide　hierarchical　representations　of　the　images.　Spatial　Pyramid　Matching　(SPM)　strategy　is　adopted　to　divide　the　feature　map,　and　each　SPM　sub-region　is　encoded　to　generate　a　feature　representation　vector.　These　feature　representation　vectors　of　convolutional　layers　and　the　output　feature　vector　of　the　global　pooling　layer　are　normalized　and　cascaded　as　a　whole　feature　vector,　which　is　finally　utilized　to　train　Support　Vector　Machine　classifier　to　obtain　the　recognition　model.　The　experimental　results　show　that,　compared　with　the　state-of-art　methods,　the　proposed　method　can　obtain　more　than　0.7%　higher　recognition　accuracy,　up　to　95.41%,　while　the　dimensionality　of　the　feature　vector　is　only　18%　and　the　memory　footprint　is　only　0.5%.　(C)　2018　Elsevier　B.V.　All　rights　reserved.