Feeder　detection　for　single-phase　ground　fault　(SPGF)　is　challenging　in　a　resonant　grounded　system　due　to　the　difference　in　feeder　capacitance　to　ground　and　the　influence　of　the　arc　suppression　coil.　This　article　uses　semantic　segmentation　algorithms　to　implement　feeder　detection　for　SPGF　in　distribution　networks.　The　proposed　method　overlays　transient　zero-sequence　voltage　(ZSV)　derivatives　and　transient　zero-sequence　current　(ZSC)　waveforms　on　the　same　image.　Then,　a　semantic　segmentation　algorithm　is　used　to　classify　the　pixel　points　of　the　image.　The　segmentation　map　output　by　the　semantic　segmentation　algorithm　contains　category　prediction　results　for　each　pixel　in　the　input　image.　Detecting　faulty　feeders　based　on　the　number　of　pixels　of　different　categories　in　the　segmentation　map　can　make　the　final　decision-making　process　more　transparent　and　easy　to　understand.　The　validity　and　adaptability　of　the　proposed　method　have　been　confirmed　through　tests　using　both　simulation　and　field　data.　The　proposed　method　achieves　an　accuracy　of　over　95%　on　simulated　data,　even　in　the　presence　of　noise　interference　and　asynchronous　sampling,　etc.　The　proposed　method,　furthermore,　achieves　an　accuracy　of　over　99%　when　applied　to　full-scale　test　data.