Detecting　wood　broken　defects　through　machine　vision　is　challenging　due　to　the　similar　appearance　of　defect　and　defect-free　regions　on　images.　Laser　profilometer　is　a　reasonable　solution,　nevertheless,　imperfect　point　cloud　representation,　such　as　slope　profile,　incontinuity　of　tiny　defects　and　similarity　between　broken　defects　and　sound　area,　poses　obstacles.　To　overcome　these　challenges,　this　study　proposes　a　multi-line　detection　method　based　on　bidirectional　long-　and　short-term　memory　network　(Bi-LSTM)　for　real-time　wood　broken　defect　detection.　The　feature　that　represents　the　extent　of　surface　damage　in　line-level　is　designed　by　residual　extraction　and　sorting　operation.　The　Bi-LSTM　combines　adjacent　information　to　exaggerate　semantic　information　of　detection　line.　Context　information　extracted　by　Bi-LSTM　are　concatenated　for　multi-line　detection　to　reduce　computation　complexity.　Finally,　detection　results　are　modified　by　considering　the　information　of　adjacent　lines　of　point　cloud.　Experimental　results　show　that　the　proposed　method　achieves　real-time　detection　with　high　accuracy.　©　2023　Elsevier　Ltd