In　this　study,　we　propose　a　reparameterization　You-Only-Look-Once　v5　(YOLOv5)　algorithm　model　for　strip-steel　surface　defect　detection　to　address　low　precision　and　poor　timeliness　in　traditional　methods.　The　proposed　model　introduces　a　re-parameterized　VGG　Light　module,　an　enhanced　bidirectional　feature　pyramid　network　feature　structure,　and　a　bounding　box　regression　loss　function　fused　with　a　normalized　Gaussian-Wasserstein　distance　metric　to　improve　small-target-defect　detection　accuracy.　The　experimental　findings　reveal　a　mean　average　precision　(mAP)　of　82.1%　on　the　NEU-DET　dataset,　representing　a　notable　improvement　of　4.1%　over　the　baseline　YOLOv5s　algorithm.　Furthermore,　the　proposed　algorithm　model　demonstrates　superior　detection　accuracy　compared　with　other　prevalent　object　detection　models　and　effectively　mitigates　challenges　such　as　false　detections　and　missed　detections　of　small　targets.　Notably,　it　achieves　an　impressive　detection　speed　of　68　FPS,　affirming　its　efficacy　in　real-time　applications.