Defect　detection　is　a　very　important　topic　in　industrial　inspection.　Due　to　the　rapid　expansion　of　computer　computing　power,　manual-based　defect　detection　approaches　is　no　longer　satisfactory,　and　deep　learning-based　detection　techniques　are　increasingly　being　applied　to　defect　detection　tasks.　contrastive　learning　is　better　for　defect　detection　tasks　due　to　the　advantages　of　its　label-free　training　method.　So　the　Efficient　Fabric　Defect　Detector　(EFDD),　a　high-efficiency　defect　detector　based　on　contrastive　learning　approach　and　layered　fusion　network,　is　proposed　in　our　paper.　First,　we　propose　the　Layered　Fusion　network　to　encode　defect　types　with　different　scale　sizes　and　improve　the　feature　extraction　ability　of　the　model.　Second,　a　universal　structure　with　an　attention　module　is　introduced,　this　module　can　be　integrated　into　ResNet　with　little　overhead.　Third,　we　investigate　the　impact　of　parallel　enhancement　strategies　on　model　training　in　the　data　imbalance,　different　combinations　can　affect　the　model　to　different　degrees.　Finally,　we　evaluate　the　model　using　three　classical　public　datasets　and　the　average　improvement　of　EFDD　over　the　three　datasets　is　AP　2.1%,　AR　1.5%　compared　to　other　contrastive　learning　methods.　Compared　to　the　supervised　methods,　our　model　accuracy　is　close　to　or　even　exceeds　that　of　the　supervised　methods.　　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.