Detection　of　road　damage　is　critical　for　improving　traffic　safety　and　extending　the　life　of　roads.　Despite　the　enhanced　accuracy　offered　by　deep　learning　models　such　as　YOLOv8　in　the　field　of　road　damage　detection,　challenges　persist　due　to　factors　like　susceptibility　to　weather　conditions　and　pavement　composition,　and　the　difficulty　in　accurately　identifying　and　pinpointing　minor　damages　in　complex　scenarios.　To　tackle　these　issues,　we　introduce　an　enhanced　detection　algorithm,　YOLOv8-LSD.　This　algorithm　incorporates　a　deformable　attention　mechanism　and　a　large　separable　kernel　attention　framework,　which　heighten　focus　on　key　regions　within　the　input　feature　map,　thereby　facilitating　precise　damage　detection.　Additionally,　it　employs　spatial　and　channel　reconstruction　in　the　detection　head　to　optimize　convolution,　ensuring　meticulous　feature　extraction　and　representation.　Experimental　findings　demonstrate　that　our　enhanced　algorithm　elevates　detection　accuracy　by　1%　over　the　original　YOLOv8　model.　©　2024　IEEE.