The　efficient　pointer　meter　reading　methods　have　been　proposed　based　on　machine　vision　to　replace　time-consuming　manual　inspections　for　the　industrial　monitoring.　However,　the　interference　factors,　such　as　rain　or　dirt,　can　occlude　meter,　which　poses　obstacles　in　the　recognition　and　labeling　of　pointer　and　scales.　To　solve　these　problems,　we　propose　a　multi-task　network　with　pointer　and　main　scale　detection　(PMSD-Net)　for　the　occluded　meter　reading　with　synthetic　data　generation　technology.　Specifically,　dense　parallel　dilated　convolution　block　is　proposed　for　correlating　the　pointer　and　main　scale　features　with　large　receptive　field.　Multi-scale　feature　fusion　is　designed　to　purify　noisy　features　for　the　detailed　information　extraction.　The　relation　reconstruction　mechanism　is　designed　to　reconstruct　the　feature　relation　under　severe　occlusion.　Moreover,　the　keypoint　detection　branch　is　designed　to　detect　meter　center　and　pointer　tip　according　to　the　segmented　pointer,　which　can　identify　changeable　position　of　the　segmented　pointer　tip　to　determine　the　pointer　orientation.　Finally,　the　synthetic　data　generation　technology　is　developed　to　generate　massive　labeled　data　with　simulated　interference　factors　in　the　meter　for　the　training,　which　enhances　the　generalization　ability　of　PMSD-Net　in　various　occlusion　scenes.　Experimental　results　indicate　that　PMSD-Net　can　segment　more　accurate　regions　of　pointer　and　main　scale　and　detect　the　changeable　position　of　pointer　tip　for　occluded　meters,　thereby　improving　the　accuracy　in　reading　occluded　meters.　©　2024　Elsevier　Ltd