Focusing　on　the　fabric　defect　detection　with　periodic-pattern　and　pure-color　texture,　an　algorithm　based　on　Direction　Template　and　Image　Pyramid　is　proposed.　The　detection　process　is　divided　into　two　stages:　model　training　and　defect　localization.　During　the　model　training　stage,　we　construct　an　Image　Pyramid　for　each　fabric　image　that　does　not　contain　any　defects.　Then,　Stacked　De-noising　Convolutional　Auto-Encoder　(SDCAE)　is　used　for　image　reconstruction,　its　training　sets　are　created　by　randomly　extracting　image　blocks　from　image　pyramid,　which　makes　the　feature　information　of　the　image　block　more　abundant　and　the　reconstruction　effect　of　the　model　more　remarkable.　During　the　defect　localization　stage,　the　image　to　be　detected　is　divided　into　a　number　of　blocks,　and　is　reconstructed　by　using　the　trained　SDCAE　model.　Then,　the　candidate　defective　image　blocks　are　roughly　located　by　using　the　Structural　Similarity　Index　Measurement　after　the　image　reconstruction.　Subsequently,　direction　template　is　introduced　to　solve　the　problem　of　fabric　deformation　caused　by　factors　such　as　fabric　production　environment　and　photographic　angle.　We　select　the　direction　template　of　the　images　to　be　detected,　filter　the　candidate　defective　blocks,　and　further　reduce　false　detection　rate　of　the　proposed　algorithm.　Furthermore,　there　is　no　need　to　calculate　size　of　periodic-pattern　during　detection　for　periodic　textured　fabric.　The　algorithm　is　also　suitable　for　defect　detection　for　pure-color　fabrics.　The　experimental　results　show　that　the　proposed　algorithm　can　achieve　better　defect　localization　accuracy,　and　receive　better　results　in　detection　of　pure-color　fabrics,　compared　with　traditional　methods.　©　2013　IEEE.