To　address　the　challenges　posed　by　limited　sample　sizes　and　varying　defect　sizes　on　strip-steel　surfaces　in　industrial　applications,　in　this　paper,　we　introduce　a　small-object　detection　you-only-look-once　(YOLO)　network　(SODY-Net)　specifically　designed　for　such　surfaces　by　machine　learning　technology.　Initially,　we　build　upon　the　YOLOv5s　framework　and　develop　a　multiscale　path　aggregation　network　that　incorporates　an　attention　mechanism　to　improve　the　model＇s　capability　to　predict　across　multiple　scales.　Next,　we　present　an　adaptive　coordinate-decoupled　head　module　for　resolving　the　conflict　between　the　classification　and　regression　tasks.　Finally,　we　propose　a　bounding　box　regression　loss　function　that　integrates　the　Wasserstein　distance　to　enhance　detection　accuracy　for　small　defects.　Experimental　results　indicate　that　our　SODY-Net　surpasses　other　small-object　detection　frameworks　when　evaluated　on　a　few-shot　dataset　of　strip-steel　surface　defects,　making　it　particularly　suitable　for　defect　detection　tasks　in　industrial　settings.