Vehicle　re-identification　seeks　to　identify　all　images　of　vehicles　with　the　same　identity　as　a　queried　vehicle　in　a　large　database.　Current　algorithms　face　challenges　of　low　accuracy　and　model　redundancy　due　to　varying　vehicle　appearances　from　different　angles　and　minimal　differences　among　different　vehicles　in　specific　views.　This　paper　proposes　an　end-to-end　dual-branch　vehicle　re-identification　model　with　both　global　and　local　feature　enhancements.　The　network　aims　to　boost　the　representation　capability　of　vehicle　features　and　obtain　more　comprehensive　and　distinctive　vehicle　characteristics　through　the　fusion　of　dual-branch　features.　The　model　is　divided　into　two　branches　representing　global　and　local　features　respectively.　A　spatial　scale　enhancement　module　is　employed　in　the　global　feature　branch　to　enhance　the　perception　of　spatial　information　of　vehicles.　In　the　local　branch,　a　strategy　of　feature　map　segmentation　in　the　middle　layers　of　the　network　is　utilized　to　encourage　the　model　to　explore　more　crucial　local　details.　The　model　is　trained　using　a　combination　of　two　loss　functions　to　enhance　the　recognition　capability　of　the　network.Experimental　results　demonstrate　that　the　proposed　model　achieves　an　mAP　of　84.5%　and　CMC@1　of　97.6%　on　the　VeRi-776　dataset.　On　the　VehicleID　dataset,　the　CMC@1　values　for　the　three　sub-datasets　are　87.2%,　84.6%,　and　81.3%,　respectively,　surpassing　advanced　algorithms　such　as　PVEN,　SSBVER,　and　TANet.The　research　findings　affirm　the　advancement　and　effectiveness　of　the　proposed　model.　©　2024　IEEE.