Deep　learning　technology　has　advanced　rapidly　and　has　started　to　be　applied　for　the　detection　of　welding　defects.　In　the　manufacturing　process　of　power　batteries　for　new　energy　vehicles,　welding　defects　may　occur　due　to　the　high　directivity,　convergence,　and　penetration　of　the　laser　beam.　The　accuracy　of　deep　learning　prediction　relies　heavily　on　big　data,　but　balanced　big　data　of　welding　defects　is　hard　to　acquire　at　the　battery　production　site.　In　this　paper,　the　authors　construct　a　dataset　named　RIAM,　which　consists　of　images　captured　from　an　industrial　environment　for　laser　welding　of　power　battery　modules.　RIAM　contains　four　types　of　images:　Normality,　Lack　of　fusion,　Surface　porosity,　and　Scaled　surface.　The　characteristics　of　RIAM　are　carefully　considered　in　the　application　scenarios.　Moreover,　this　paper　proposes　a　gradient-based　unsupervised　model　named　Grad-MobileNet,　which　can　be　trained　with　only　a　few　normal　images　and　can　extract　the　feature　gradients　of　the　input　images.　Welding　defects　can　then　be　classified　by　the　gradient　distribution.　This　model　is　based　on　MobileNetV3,　which　is　a　lightweight　convolutional　neural　network　(CNN),　and　achieves　99%　accuracy,　which　is　higher　than　the　accuracy　expected　from　supervised　learning.