Wooden　boards　are　widely　used　as　raw　materials　for　furniture.　Detecting　defects　on　the　wooden　is　significant　to　improve　the　quality　of　products.　In　this　paper,　we　propose　an　improved　YOLOv4-tiny　target　detection　algorithm　with　feature　extraction　and　utilization　tailored　for　wood　defect　recognition.　The　proposed　algorithm　replace　the　residual　network　of　the　original　algorithm　with　the　Res2Net　network　to　enhance　the　feature　extraction　ability,　and　increase　the　different　scales　to　improve　the　prediction　accuracy.　A　testbed　is　deployed　to　collect　the　images　of　wooden　boards,　and　the　performance　of　the　proposed　algorithm　is　evaluated　on　the　testbed.　Compared　with　the　original　YOLOv4-tiny　algorithm,　the　mean　average　precision　for　detecting　the　three　types　of　defects　deadknot,　insectpest　and　crack　was　80.06%　which　was　an　increase　of　5%.　Compared　with　other　methods　of　wood　defect　detection,　the　proposed　algorithm　is　more　accurate　and　faster.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.