In　order　to　remove　the　limitations　of　human　interpretation,　many　computer-aided　algorithms　have　been　developed　to　automatically　detect　defects　in　radiographic　images.　Compared　with　traditional　detection　algorithms,　deep　learning　algorithms　have　the　advantages　of　strong　generalization　ability　and　automatic　feature　extraction,　and　have　been　applied　in　welding　defect　detection.　However,　these　algorithms　still　need　further　research　in　the　acquisition　and　cleaning　of　welding　radiographic　image　data,　the　selection　and　optimization　of　deep　neural　networks,　and　the　generalization　and　interpretation　of　network　models.　Therefore,　this　paper　proposes　an　automatic　welding　defect　detection　system　based　on　semantic　segmentation　method.　Firstly,　a　dataset　of　radiographic　images　of　welding　defects,　called　RIWD,　is　set　up,　and　the　corresponding　data　preprocessing　and　annotation　methods　are　designed　for　the　training　and　evaluation　of　the　algorithm.　Secondly,　an　end-to-end　FPN-ResNet-34　semantic　segmentation　network-based　defect　detection　algorithm　is　implemented,　and　the　network　architecture　is　experimentally　demonstrated　to　be　suitable　for　defect　features　extraction　and　fusion.　Thirdly,　to　improve　the　detection　performance　of　the　algorithm,　an　optimization　strategy　for　the　network　is　designed　according　to　the　data　characteristics　of　defects,　which　includes　data　augmentation　based　on　combined　image　transformations　and　class　balancing　using　a　hybrid　loss　function　with　dice　loss　and　focal　loss.　Finally,　to　ensure　the　reliability　of　the　algorithm,　the　generalization　ability　of　the　algorithm　is　tested　using　external　validation,　and　the　defect　features　learned　by　the　network　are　visualized　by　post-interpretation　technique.　The　experimental　results　show　that　our　method　can　correctly　discriminate　defect　types　and　accurately　describe　defect　boundaries,　achieving　0.90　mPA,　0.86　mR,　0.77　mF1　and　0.73　mIoU,　which　can　be　applied　to　automatically　interpret　radiographic　images.