Anomaly　detection　in　industrial　manufacturing　faces　challenges　such　as　limited　training　data,　noise　interference　and　inefficiency　of　single-model　architecture.　To　deal　with　these　problems,　this　article　proposes　a　cross-architecture　knowledge　distillation　framework　in　which　a　transformer　(DINOv2-small)　acts　as　a　teacher　network　and　a　lightweight　CNN　(ResNet18)　acts　as　a　student　network,　connected　by　ResNet　block　adapters　to　address　the　gradient　mismatch　problem　of　heterogeneous　architectures.　The　cross-attention　mechanism　further　enhances　multi-level　feature　migration　through　query-key-value　interaction.　Evaluation　on　the　VisA　dataset　shows　that　our　approach　produces　cutting-edge　results:　image-level　AUROC　of　94.5%,　pixel-level　AUROC　of　98.6%,　pixel-level　AP　of　47.1%,　and　region-level　AUPRO　of　93.3%,　while　maintaining　an　inference　time　of　12　seconds.　Compared　with　existing　methods,　the　model　exhibits　stronger　robustness　and　localization　accuracy　in　complex　textures　(e.g.,　＇cashewc＇)　and　continuous　defective　regions　(e.g.,　＇pipe　-　fryumc＇).　This　study　provides　a　low-cost　real-time　solution　for　industrial　inspection　that　balances　efficiency　and　accuracy.　Future　work　should　explore　channel　pruning　and　quantization　for　further　optimization.　©　2025　IEEE.