The　accuracy　of　defect　detection　is　increased　by　the　improved　Faster　R-CNN　detection　algorithm,　which　addresses　the　issue　of　an　extended　target　scale　span　and　complex　characteristics　causing　missed　detection　in　steel　surface　defect　detection.　The　algorithm　introduces　path　aggregation　network　(PANet),　which　uses　bottom-up　path　aggregation　to　combine　shallow　details　and　deep　semantic　information　to　better　capture　features　on　various　dimensions.　It　also　adapts　the　reconstruction　of　the　anchor　box　size　information　to　filter　out　the　optimal　proposal　box　for　more　accurate　localization.　The　backbone　network　of　the　original　Faster　R-CNN　is　replaced　with　an　improved　ResNet50,　which　has　more　powerful　feature　extraction　skill　and　makes　the　model　more　flexible.　On　the　NEU-DET　dataset,　the　improved　algorithm＇s　detection　performance　is　contrasted　with　that　of　other　detection　algorithms.　According　to　the　results　of　the　investigation,　the　Faster　R-CNN　with　additions　has　an　average　detection　rate　of　63.7　frames　per　second　(FPS)　and　a　mean　average　precision　(mAP)　of　80.2%,　which　is　8.7　percentage　points　higher　than　that　of　the　Faster　R-CNN.　The　conclusion　is　that　the　boosted　Faster　R-CNN　can　enhance　both　the　precision　and　localization　information　of　multi-scale　faulty　targets.　©　2023　IEEE.