Intelligent　transportation　is　an　indispensable　part　of　the　smart　city　and　the　primary　development　direction　of　the　future　transportation　systems.　Vehicle　detection　and　recognition,　which　is　one　of　the　most　important　aspects　of　intelligent　transportation,　plays　a　very　important　role　in　various　areas　of　our　daily　life;　one　such　important　area　is　criminal　investigation.　In　the　fine-grained　vehicle　type　detection　and　recognition,　several　difficult　issues　such　as　problems　in　data　acquisition　and　tagging,　dramatic　variance　in　the　data　of　different　vehicle　types,　and　challenges　in　identifying　vehicles　of　the　same　brand　with　highly　similar　appearances　remain　unsolved.　For　the　problems　of　data　acquisition　and　tagging,　this　paper　presents　a　strategy　for　automatic　data　acquisition　and　tagging　based　on　object　detection　that　can　label　the　vehicle　images　efficiently　while　rapidly　acquiring　all　types　of　fine-grained　models.　Considering　the　problem　of　data　imbalance　in　the　training　process,　this　paper　proposes　a　Faster-RCNN　based　data　equalizing　strategy　(Faster-BRCNN),　thereby　improving　the　performance　of　object　detection.　In　view　of　the　severe　information　attenuation　caused　by　the　feature　information　transfer　obstruct　between　layers　in　the　traditional　deep　learning　network,　the　lack　of　mutual　dependency　of　these　features,　and　the　inability　of　the　network　to　focus　on　the　important　region　and　characteristics,　we　propose　an　intensive　dense　attention　network　(DA-Net).　Through　its　intensive　connection　and　attention　unit,　we　enhance　the　model＇s　detection　ability.　The　proposed　method　achieves　mAP　of　94.5%　and　95.8%　in　the　Stanford　Cars　and　FZU　Cars　datasets,　respectively,　thereby　verifying　its　effectiveness.　(c)　2020ElsevierB.V.　Allrightsreserved.