基于級聯(lián)式逆殘差網(wǎng)絡(luò)的游戲圖像多模態(tài)目標(biāo)精準(zhǔn)辨識研究

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Research on game imagemultimodal objectprecise identification basedoncascadedinverseresidualnetwork

LIUJianzhi

（ShenyangLigongUniversity，Shenyang11oooo， China）

Abstract：Theobjects inthegame have complexfeaturessuch asshape，colorand texture.Inaddition，theobjects appear indiferentperspectives，scalesandpostures，allofwhichincreasethediicultyofbjectrecognition.Acascadediverse residualnetworkcanenhancethemultidimensionalfeaturesoftheobject，andcanrecognizetheobjectefectivelyeveninthe presenceofocclusion，deformation，etc.Therefore，agameimagemultimodalobjectpreciseidentificationmethod basedon cascadedinverseresidual network isproposed.Abackbonenetworkconsisting ofconvolutional layersandcascaded inverse residualmodules basedondepthwiseseparableconvolutiondesignisconstructed.Thisnetworkisused toextract thenputtd gameimage features preliminarily.Thechannelrearrngementisused toenhancetheinformation exchange among channels.The featureenancementnetworkisusedtoupsamplethefeaturemaps learnedbythebackbonenetwork.Themultimodalobject featuresareetractedincombinationwiththemulti-chanelfeaturefusion.Theobjectposition，direction，andotherinforation areoutputedbyapredictionnetwork thatcanachieveclasificationandregressontasks.Sofar，apreciseidentificationof multimodalobjects ingameimagesisachieved.Theexperimentalresultsshowthatthemethodcanachievetheidentificationof thecharacters，text，scene elements andother objects in the gameimages，with atraining lossofonlyabout O.O5andan F1 -score of 0.967. To sum up，the multimodal object recognition effect of game images is good.

Keywords：inverseresidual;；gameimage;；multimodal；channelrearrangement;SIoUlossfunction；objectidentification; convolutional layer;object location

0 引言

度不斷提高，為玩家?guī)砹烁鼮檎鎸?shí)和沉浸式的體驗(yàn)，但同時(shí)也給游戲圖像目標(biāo)辨識技術(shù)提出了更高的挑戰(zhàn)。（剩余5474字）