In　the　modern　manufacturing　industry,　the　surface　defect　detection　of　steel　is　very　important　to　ensure　product　quality.　Based　on　YOLOv7-tiny　algorithm,　this　study　improves　the　real-time　and　accuracy　of　steel　surface　defect　detection　through　a　series　of　innovative　improvements.　First,　we　replace　the　original　ordinary　convolution　structure　by　introducing　Omni-dimensional　dynamic　convolution　module,　enhance　feature　extraction　to　boost　model　detection　accuracy.　Change　the　activation　function　from　Leaky　ReLU　to　SiLU,　and　enhance　the　gradient　flow　of　the　model,　improve　detection　performance.　In　addition,　by　introducing　SimAM　module　and　improving　the　performance　of　detection　headers　through　TSCODE,　the　model＇s　ability　to　recognize　small　size　targets　is　enhanced.　After　that,　using　MPDIoU　instead　of　CIoU　as　the　loss　function,　which　simplified　the　calculation　process　and　the　efficiency　and　accuracy　of　defect　detection　are　improved.　Finally,　we　verified　the　model　through　the　NEU-DET　data　set.　The　experimental　results　show　that,　with　these　improvements,　the　mAP@0.5　of　the　model　is　significantly　improved,　reaching　0.768　in　the　end,　and　the　reasoning　speed　is　also　up　to　34.5　fps,　which　proves　the　speed　and　accuracy　of　the　improved　model　in　real-time　detection　of　steel　surface　defects.　©　2024　IEEE.