基于视觉－语言大模型的实木家具选料优选方法

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中图分类号：S781.1 文献标志码：A

Abstract： 【Objective】Wood selection represents a crucial stage preceding the manufacturing processof solid wood furniture. Cuentlythldodoclsdtlalrstetetsel hasbeendevisedThisodeliscapableofsignifcantlyenhancingtheaccuracyofsolidwoodidentification，threbyestablisgaolid foundationforensuringthequalityandqualityrelatedaspectsofwoodenfurniture.【Method】Fourtypesofwoodfrequentlyemployed insolidwooduiture，melyishpine，archechndashwereelectedasteseachbectsasedoesace characteristicssuchslordtextueofteodinedstictsoldoodatasesweeconstructed.Theaskofsoldodfuue materialselectionwasaomplishedbyleveragigthetransferleaingapproachandtheimage-textpairpredictionandclasification capabilitiesofthevisualanguagelargemodelTofurtheraugmentherecognitionaccuracyofwoodforsolidwoodfuriture，the contrastivelanguage image pre-training （CLIP）visuallanguagelarge model was introducedandoptimizedusing Linear Probing. Thisoptimizationenabledtheeficientidentificationandclasifcationofmultiplesurfacefeaturesofthewood【Result】Atotalof 1 740 solid wood images were randomly partitioned into training and test sets at a ratio of 3：1 and then compared and analyzed with theResNet10lnetworkandFullFine-Tning.ByadoptingtheliearprobingmethodwithintheCLIPmodel，theoveralrcoition accuracyof themodel reach 98.29% ，themAP is 96.72% ，theiterative parameter size of themodel is 0.26MB ，and the output time per image is 37.48ms .Both theclassification performance and speed areoptimized.【Conclusion】The results indicated that in the task of solidwoodfuiturematerialselectionwithlimitedsampletrainng，theleaingperformaneandstabilityofteproposedmetodave beenefctivelyliatdIoldstottialfpiaoitellgntodleioipmntchielyo woodutilizationrateandtheproductionefciecyofsolidwoodfuiture.Moreover，itprovdessigfcantgudaceforpromotingthe intelligent manufacturing of solid wood furniture.

Keywords：solidwoodfurniturematerialselection;visuallanguagelarge model; linearprobing;inteligent manufacturing

家具作为人们日常生活中的必需品，随着生活水平的不断提升，也从基础功能需求转向品质与审美兼具。（剩余13110字）