Wood　surface　defect　segmentation　is　extremely　critical　for　defect　refinement　and　quality　control　of　wooden　products.　However,　it　is　a　challenging　task　to　develop　an　efficient　method　with　current　algorithms　due　to　the　complicated　characteristics　of　wood　defects　with　obscure　boundary,　intraclass　difference　and　interclass　similarity.　To　address　these　issues,　a　lightweight　network　via　multi-dimension　boundary　perception　and　guidance　is　proposed　for　precise　segmentation　of　wood　defects.　At　first,　based　on　the　Segformer,　a　boundary　prediction　branch　is　added　to　enrich　detailed　boundary　information　in　the　encoder,　and　supervised　by　the　Gaussian　signal　and　cosine　similarity,　to　balance　the　effect　of　the　boundary　gradient　information.　Then,　a　double-flow　enhancing　module　is　designed　to　integrate　the　adjacent　level　features,　by　embedding　two　enhancing　paths,　to　adaptively　generate　discriminative　information　of　the　defects.　Finally,　a　binary　segmentation　head　following　the　predicted　map　is　introduced　to　strengthen　the　penalty　for　the　false　prediction　results　of　the　boundary.　Experimental　results　demonstrate　the　proposed　method　outperforms　the　state-of-the-arts　on　our　wood　surface　defect　dataset,　as　well　as　on　three　public　datasets.