Surface　defect　detection　of　sawn　timber　is　a　critical　task　to　ensure　the　quality　of　wooden　products.　Current　methods　have　challenges　in　considering　detection　accuracy　and　speed　simultaneously,　due　to　the　complexity　of　defects　and　the　massive　length　of　sawn　timbers.　Specifically,　there　are　scale　variation,　large　intraclass　difference　and　high　interclass　similarity　in　the　defects,　which　reduce　the　detection　accuracy.　To　overcome　these　challenges,　we　propose　an　efficient　multilevel-feature　integration　network　(EMINet)　based　on　YOLOv5s.　To　obtain　discriminative　features　of　defects,　the　cross　fusion　module　(CFM)　is　proposed　to　fully　integrate　the　multilevel　features　of　backbone.　In　the　CFM,　the　local　information　aggregation　is　designed　to　enrich　the　detailed　information　of　high-level　features,　and　the　global　information　aggregation　is　designed　to　enhance　the　semantic　information　of　low-level　features.　Experimental　results　demonstrate　that　the　proposed　EMINet　achieves　better　accuracy　with　fast　speed　compared　with　the　state-of-the-art　methods.