Pedestrian　detection　and　re-identification　technology　is　a　research　hotspot　in　the　field　of　computer　vision.　This　technology　currently　has　issues　such　as　insufficient　pedestrian　expression　ability,　occlusion,　diverse　pedestrian　attitude,　and　difficulty　of　small-scale　pedestrian　detection.　In　this　paper,　we　proposed　an　end-to-end　pedestrian　detection　and　re-identification　model　in　real　scenes,　which　can　effectively　solve　these　problems.　In　our　model,　the　original　images　are　processed　with　a　non-overlapped　image　blocking　data　augmentation　method,　and　then　input　them　into　the　YOLOv3　detector　to　obtain　the　object　position　information.　LCNN-based　pedestrian　re-identification　model　is　used　to　extract　the　features　of　the　object.　Furthermore,　the　eigenvectors　of　the　object　and　the　detected　pedestrians　are　calculated,　and　the　similarity　between　them　are　used　to　determine　whether　they　can　be　marked　as　target　pedestrians.　Our　method　is　lightweight　and　end-to-end,　which　can　be　applied　to　the　real　scenes.