With　the　rapid　advancement　of　automation　and　intelligence　in　the　electronics　manufacturing　industry,　the　throughput　of　a　single　production　line　was　grown　exponentially.　Although　high　efficiency　brought　significant　cost　and　time　advantages,　it　also　led　to　two　major　challenges:　(1)　extremely　low　tolerance　for　error—any　slight　defect　might　have　caused　the　entire　product　to　be　scrapped;　(2)　increasingly　diverse　and　more　concealed　types　of　defects—bubble　defects,　internal　chip　defects,　printed　circuit　board　(PCB)　defects,　and　specific　process　defects　were　continuously　emerged,　posing　significant　challenges　to　the　inspection　process.　Traditional　manual　visual　inspection　or　single-task　deep　learning　models　were　often　struggled　to　balance　detection　efficiency　and　accuracy　in　complex　industrial　scenarios.　To　address　the　above　challenges,　a　single-stage　industrial　defect　detection　model　based　on　multi-dataset　mixed　training—MSAN-Net—was　proposed　in　this　paper.　Representative　datasets　covering　the　typical　scenarios　mentioned　above　were　collected　and　organized,　and　part　of　the　data　was　re-annotated　to　ensure　a　high　level　of　consistency　with　actual　production　environments.　MSAN-Net　was　adopted　an　integrated　architecture,　deeply　combining　UnifiedViT,　C2f　modules,　convolution　operations,　SPPF　structure,　and　Bi-Level　Routing　Attention　mechanism　to　achieve　accurate　identification　of　complex　industrial　defects.　Extensive　experiments　(including　comparisons　with　multiple　methods,　ablation　studies,　and　external　validation)　showed　that　MSAN-Net　was　outperformed　existing　SOTA　models　in　industrial　defect　detection　tasks,　significantly　improving　detection　accuracy　for　multi-class　defects　in　complex　scenarios,　reducing　reliance　on　manual　inspection,　and　effectively　lowering　scrap　losses　caused　by　defects,　thus　providing　a　reliable　solution　for　intelligent　quality　inspection　in　the　electronics　manufacturing　industry.　©　2013　IEEE.