To　address　the　challenge　of　low　detection　accuracy　of　small　targets,　such　as　cyclists　and　pedestrians　in　the　PointPillars　algorithm,　an　improved　PointPillars　algorithm　based　on　point　cloud　feature　enhancement　is　proposed.　First,　the　quality　of　the　input　point　cloud　data　is　improved　by　increasing　the　environmental　density　aware　sampling　and　IFPS　point　cloud　sparsity.　Second,　a　stepped　ECA　attention　mechanism　is　integrated　into　the　point　cloud　feature　encoding　to　enhance　the　point　cloud　features　through　multilevel　attention　guidance,　and　a　feature　fusion　enhancement　module　is　added　to　the　backbone　network　to　strengthen　the　interaction　between　feature　maps　at　different　levels.　Finally,　the　introduction　of　the　EMA　attention　mechanism　further　enhances　the　point　cloud　features　in　the　feature　map.　The　experimental　results　based　on　the　KITTI　dataset　indicate　that　the　proposed　improved　algorithm　improves　the　three-dimensional　average　detection　accuracy　of　pedestrians　and　cyclists　in　simple,　moderate,　and　difficult　scenarios　by　7.　9　percentage　points,　8.　6　percentage　points,　8.　3　percentage　points,　and　4.　0　percentage　points,　2.　9　percentage　points,　3.　8　percentage　points,　respectively,　compared　to　the　original　algorithm.　Furthermore,　the　average　directional　similarity　increases　by　7.　5　percentage　points,　7.　9　percentage　points,　5.　9　percentage　points,　and　4.　4　percentage　points,　5.　2　percentage　points,　6.　1　percentage　points,　respectively.　©　2025　Universitat　zu　Koln.　All　rights　reserved.