PurposeBecause　of　the　advantages　of　high　deposition　efficiency　and　low　manufacturing　cost　compared　with　other　additive　technologies,　robotic　wire　arc　additive　manufacturing　(WAAM)　technology　has　been　widely　applied　for　fabricating　medium-　to　large-scale　metallic　components.　The　additive　manufacturing　(AM)　method　is　a　relatively　complex　process,　which　involves　the　workpiece　modeling,　conversion　of　the　model　file,　slicing,　path　planning　and　so　on.　Then　the　structure　is　formed　by　the　accumulated　weld　bead.　However,　the　poor　forming　accuracy　of　WAAM　usually　leads　to　severe　dimensional　deviation　between　the　as-built　and　the　predesigned　structures.　This　paper　aims　to　propose　a　visual　sensing　technology　and　deep　learning-assisted　WAAM　method　for　fabricating　metallic　structure,　to　simplify　the　complex　WAAM　process　and　improve　the　forming　accuracy.Design/methodology/approachInstead　of　slicing　of　the　workpiece　modeling　and　generating　all　the　welding　torch　paths　in　advance　of　the　fabricating　process,　this　method　is　carried　out　by　adding　the　feature　point　regression　branch　into　the　Yolov5　algorithm,　to　detect　the　feature　point　from　the　images　of　the　as-built　structure.　The　coordinates　of　the　feature　points　of　each　deposition　layer　can　be　calculated　automatically.　Then　the　welding　torch　trajectory　for　the　next　deposition　layer　is　generated　based　on　the　position　of　feature　point.FindingsThe　mean　average　precision　score　of　modified　YOLOv5　detector　is　99.5%.　Two　types　of　overhanging　structures　have　been　fabricated　by　the　proposed　method.　The　center　contour　error　between　the　actual　and　theoretical　is　0.56　and　0.27　mm　in　width　direction,　and　0.43　and　0.23　mm　in　height　direction,　respectively.Originality/valueThe　fabrication　of　circular　overhanging　structures　without　using　the　complicate　slicing　strategy,　turning　table　or　other　extra　support　verified　the　possibility　of　the　robotic　WAAM　system　with　deep　learning　technology.