Vehicle　classification　is　vital　to　an　intelligent　transport　system.　To　obtain　a　high　accuracy,　it　is　the　most　crucial　process　to　extract　reliable　and　distinguishable　features　of　vehicles.　A　feature　extraction　method　using　a　lightweight　convolutional　network　for　vehicle　classification　is　proposed.　The　main　contributions　are　threefold:　(1)　a　lightweight　network　named　LWNet　with　two　convolution　layers　is　proposed　to　extract　the　features　of　the　vehicles;　(2)　Hu　moment　is　integrated　with　spatial　location　information　to　improve　its　own　describing　and　distinguishing　ability;　and　(3)　histogram　of　oriented　gradient　(HOG)　feature　is　extracted　from　the　complete　image,　and　then　the　above　two　kinds　of　features　are　combined　with　HOG　to　form　the　vector.　And　then,　a　support　vector　machine　is　trained　to　obtain　the　classification　model.　Vehicles　are　classified　into　six　categories,　i.e.,　large　bus,　car,　motorcycle,　minibus,　truck,　and　van.　The　experimental　results　have　demonstrated　that　the　classification　accuracy　can　achieve　97.39%,　which　is　3.81%　higher　than　that　obtained　from　the　conventional　methods.　In　addition,　for　this　vehicle　classification　task,　the　proposed　lightweight　convolutional　network　can　achieve　comparable　or　even　higher　performance　compared　to　the　deep　convolutional　neural　networks,　while　the　proposed　method　does　not　need　the　support　of　a　graphics　processing　unit　and　has　much　lower　complexity　without　the　training　process.　(C)　2018　SPIE　and　IS&T