基于低空熱紅外影像的城市軌道交通風(fēng)險(xiǎn)安全監(jiān)護(hù)關(guān)鍵技術(shù)

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中圖分類號：P631.7;U293.6 文獻(xiàn)標(biāo)志碼：A 文章編號：2095-1329（2025）02-0065-07

Abstract：Ispeoofailtansitsetyprotectioonsisiticalmeasuefoesuringurbanailtasitsaety.Howeveincoplex noctumalenvironents，traditioalmanalinspectiosandUAVvisiblelightechnoloiesstruggletoetispctionqts.o adresthesecallnges，tispaperproposesaintellgentmoitoringmetodintegratigteralinfraredimageryechnologywiththe YOLOv5 deep learing algorithm.The approach involves equippng unmanned aerial vehicles （UAVs）with thermal infrared cameras for nocturnalimageacquisitioninprotectionzones.TheYOLOv5 modelisemployedfortarget detectionofconstructionequipment intheralinfraredimagery，wilethermalanomalyfeatureextractio，dynamicbackground modeling，andamulti-ruleclasifier arecomprehensivelyappliedtoachieveautomaticidentificationand judgmentofconstruction equipment tatus.This studyselects theLiuchenRoadDepotofShanghaiMetroLine21astheexperimentalarea，conducting testresearchthroughroutedesign，dataset preparation，oeltngdpfoaceaatExpetalsultsemostratecuracipententti statusmonitoring，with theoverall model algorithmprecision reaching 94.33% andaKappacoefficientofO.89.Thismethod effectively fulfillsthedemandsofoctualailtransitispectios，enablingprciserecogitionofnightieconstructionequipmentsafety protectionzones，earlywamingofabnoralenvironmentaltemperaturechanges，anddiscriminationofequpmentoperatingstates.It provides a feasible technical solution for implementing nocturnal inspections of urban rail transit systems.

Keywords：rail tansitsfetyprotectionzoneralinfraredimageryUAV（unmanederialeicle）deepleaingiteliet monitoring

截至2024年底，上海軌道交通已形成896公里運(yùn)營里程、日均客運(yùn)量突破1027萬人次的超大規(guī)模系統(tǒng)[，線路兩側(cè)50米（地下段）范圍為軌道交通安全保護(hù)區(qū)，保護(hù)區(qū)內(nèi)的違規(guī)施工、卸載、加載均會造成隧道變形、漏水甚至破損[2。（剩余9101字）