Automated　wood　defect　detection　is　of　great　significance　for　improving　wood　utilization.　However,　the　stains　on　the　wood　surface　resemble　defect　features　and　it　is　difficult　to　distinguish　the　appearance　of　wane　from　defect-free　regions.　Relying　solely　on　machine　vision　for　extracting　surface　features　of　wood　makes　it　difficult　to　achieve　accurate　defect　detection.　To　address　these　challenges,　we　propose　MMDNet,　a　multimodal　detection　network　that　combines　point　cloud　and　image　data.　By　adaptively　fusing　point　cloud　and　image　features　at　multiple　stages,　the　network　effectively　highlights　wood　surface　defect　regions.　Additionally,　we　introduce　an　Atrous　Spatial　Pyramid　Pooling　(ASPP)　module　into　the　network,　which　expands　the　network＇s　receptive　field　and　enables　a　comprehensive　perception　of　the　defects.　Furthermore,　we　employ　a　deep　supervision　strategy　to　encourage　the　network　to　learn　discriminative　feature　representations,　enhancing　the　model＇s　ability　to　differentiate　defect　regions.　Experimental　results　validate　the　effectiveness　of　our　method　in　detecting　defects　that　are　similar　to　the　background　and　reducing　interference　from　stains.　©　2023　IEEE.