To　address　the　issue　of　low　recognition　accuracy　in　lightweight　algorithms　for　steel　surface　defect　detection,　this　paper　introduces　a　Multi-scale　Enhanced　Feature　Fusion　(EFF)　technique.　Initially,　an　Adaptive　Weighted　Fusion　(AWF)　module　calculates　fusion　weights　adaptively　for　different　feature　levels.　This　allows　shallow　features　to　enrich　with　deep　semantics　without　compromising　detail.　Subsequently,　the　Spatial　Feature　Enhancement　(SFE)　module　boosts　the　fused　features　from　three　distinct　directions　and　improves　network　stability　by　integrating　residual　pathways,　enabling　the　convolution　process　to　extract　more　critical　information.　The　model　then　selects　better　training　samples　based　on　the　overlap　between　the　prior　box　and　the　ground　truth.　Experimental　outcomes　show　that　the　proposed　method　achieves　a　detection　accuracy　of　80.47%,　marking　a　6.81%　increase　over　the　baseline　algorithm.　Moreover,　with　2.36　M　parameters　and　952.67　MFLOPs,　this　algorithm　efficiently　and　accurately　identifies　steel　surface　defects,　demonstrating　significant　practical　utility.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.