Stainless　steel　sheets　with　bright　surfaces　have　been　widely　used　in　household　appliances,　decorative　materials,　highway　and　railway　vehicles,　and　other　fields.　In　order　to　reduce　welding　deformation　while　maintaining　overall　aesthetics,　the　commonly　used　welding　method　is　laser　welding.　In　laser　welding,　various　welding　defects　are　the　main　causes　of　weld　failure.　At　present,　the　main　detection　method　for　defects　in　such　slender　welds　is　laser　structured　light　visual　scanning　detection.　However,　due　to　the　fact　that　the　reflection　of　high　gloss　stainless　steel　material　is　mainly　mirror　reflection,　there　will　be　a　substantial　amount　of　signal　loss　in　structured　light　visual　inspection.　This　article　proposes　a　characterization　and　detection　method　for　the　surface　morphology　and　defects　of　laser　welding　of　high　reflective　materials.　The　method　uses　latitude　and　longitude　RGB　light　sources　for　illumination,　which　can　form　distinct　and　different　forms　of　rainbow　bars　on　various　types　of　weld　defects,　providing　powerful　conditions　for　defect　recognition.　Unlike　laser　structured　light　vision,　this　method　fully　utilizes　the　specular　reflection　characteristics　of　the　material　surface　and　has　a　faster　detection　speed.　This　article　uses　a　ball　model　to　reveal　the　decoupled　correspondence　between　material　surface　color　and　gradient　direction　and　magnitude　under　latitude-distributed　and　longitude-distributed　RGB　light　source　illumination,　explaining　the　mechanism　and　reliability　of　the　detection　method,　and　providing　conditions　for　further　quantification　of　defects.　Finally,　this　article　combines　U-Net　network　and　its　various　improved　versions　to　achieve　high-precision　welding　defect　detection,　with　an　average　accuracy　of　89.88%　and　a　recall　rate　of　92%.