In　recent　years,　shared　bicycles　have　become　an　important　way　of　short-distance　travel　in　cities,　but　the　problem　of　random　parking　needs　to　be　efficiently　managed.　In　the　occluded　scene,　the　existing　detection　methods　are　prone　to　misdetection　or　omission　of　occluded　areas　and　small　targets　due　to　the　variable　target　morphology　caused　by　occlusion,　the　difficulty　of　feature　extraction,　and　the　loss　of　details　caused　by　downsampling.　In　this　paper,　an　improved　model　based　on　YOLOv11,　which　integrates　multi-scale　features　and　multi-head　attention　SEAM　Module,　is　proposed　to　solve　the　problem　of　occluded　target　detection　in　the　above-mentioned　shared　bicycle　images.　Firstly,　by　introducing　a　multi-scale　feature　fusion　module　based　on　deconvolution　and　replacing　the　original　upsampling　and　splicing　operations,　the　fusion　effect　of　multi-scale　features　is　enhanced,　so　that　the　model　can　more　effectively　capture　semantic　information　at　different　levels.　Secondly,　the　multi-head　attention　SEAM　(Separated　and　Enhancement　Attention　Modul)　Module　is　introduced　before　the　detection　head　to　improve　the　model＇s　ability　to　detect　occluded　targets　by　adaptively　weighting　important　features　and　suppressing　redundant　information.　Experimental　results　demonstrate　significant　improvements　in　recall　(+5.5%)　、　and　mAP50　(+3.3%),　particularly　in　complex　occlusion　scenarios,　validating　the　effectiveness　of　both　modules.　©2025　IEEE.