The　semiconductor　manufacturing　industry　relies　heavily　on　wafer　surface　defect　detection　for　yield　enhancement.　Machine　learning　and　digital　image　processing　technologies　have　been　used　in　the　development　of　various　detection　algorithms.　However,　most　wafer　surface　inspection　algorithms　are　not　be　applied　in　industrial　environments　due　to　the　difficulty　in　obtaining　training　samples,　high　computational　requirements,　and　poor　generalization.　In　order　to　overcome　these　difficulties,　this　paper　introduces　a　full-flow　inspection　method　based　on　machine　vision　to　detect　wafer　surface　defects.　Starting　with　the　die　image　segmentation　stage,　where　a　die　segmentation　algorithm　based　on　candidate　frame　fitting　and　coordinate　interpolation　is　proposed　for　die　sample　missing　matching　segmentation.　The　method　can　segment　all　the　dies　in　the　wafer,　avoiding　the　problem　of　missing　dies　splitting.　After　that,　in　the　defect　detection　stage,　we　propose　a　die　defect　anomaly　detection　method　based　on　defect　feature　clustering　by　region,　which　can　reduce　the　impact　of　noise　in　other　regions　when　extracting　defect　features　in　a　single　region.　The　experiments　show　that　the　proposed　inspection　method　can　precisely　position　and　segment　die　images,　and　find　defective　dies　with　an　accuracy　of　more　than　97%.　The　defect　detection　method　proposed　in　this　paper　can　be　applied　to　inspect　wafer　manufacturing.