[Objective]　The　phenomenon　of　segment　dislocation　is　prevalent　in　shield　tunnel　lining,　thereby　affecting　the　structural　safety　and　service　performance　of　tunnels　in　severe　scenarios.　Manual　measurement　of　segment　dislocation　is　subjective　and　time-consuming,　while　automated　methods,　such　as　3D　laser　scanners,　are　notonly　expensive　but　also　susceptible　to　environmental　conditions.　In　recent　years,　advancements　in　camera　technology　and　deep　learning　algorithms　have　significantly　propelled　the　development　of　computer　vision　applications　in　civil　engineering　monitoring　and　inspection.　Binocular　vision　technology　has　been　utilized　in　crack　detection　and　engineering　quality　evaluation　due　to　its　cost-effectiveness　and　reliable　accuracy.　This　study　introduces　binocular　vision　technology　to　enable　accurate　measurement　of　segment　dislocation.　[Methods]　The　internal　and　external　parametersof　consumer-grade　binocular　cameras　are　initially　obtained　through　a　calibration　experiment,　followed　by　capturing　binocular　images　of　the　segment　joints.　The　accurate　disparity　information　between　the　left　and　right　images　is　further　calculated　using　a　deep-learning　stereomatching　algorithm.　Finally,　a　camera　attitude　correction　method　is　proposed　to　calculate　the　segment　dislocation　accurately.　The　primary　challenge　in　camera　attitude　correction　is　determining　key　points　on　the　segmentsurface　without　evident　texture　features.　Therefore,　a　series　of　image　processing　techniques,　such　as　graying,　binarization,　dilation,　erosion,　and　target　localization,　are　initially　employed　on　the　left　photo　to　automatically　identify　the　location　of　the　segment　joint.　Then,　the　parallel　thinning　algorithm　is　applied　to　extract　the　skeleton　of　the　segment　joint　(reduce　the　segment　joint　width　from　multi-pixel　to　single-pixel).　The　pixel　coordinate　data　along　the　skeleton　are　extracted,　and　the　least　squares　method　is　used　to　fit　a　straight　line　to　the　skeleton.　Furthermore,　the　straight　lines　are　shifted　and　flipped　to　form　latitude　and　longitude　lines,　allowing　the　pixel　and　camera　coordinates　of　the　crossing　point　(key　point)　to　be　calculated　in　accordance　with　the　triangulation　principle.　Finally,　the　segment　coordinate　system　is　established　using　these　key　points,　and　the　actual　segment　dislocation　is　computed.　[Results]　Field　tests　on　the　construction　site　of　the　shield　tunnel　were　carried　out　to　evaluate　the　efficiency　of　the　proposed　approach.　Results　were　drawn　as　follows:　(1)　The　calculation　results　from　the　vertical　shooting　of　the　binocular　camera,　after　camera　attitude　correction,　showed　better　consistency　than　the　segment　dislocation　measurement　via　weld　seam　gauging.　The　specific　performance　demonstrated　that　the　absolute　error　did　not　exceed　1.0　mm,　andthe　overall　processing　time　for　the　segmented　image　was　approximately　10.00　s.　(2)In　multiple　rounds　of　tests　with　left-leaning　or　right-leaning　shooting　scenarios,　when　the　attitude　angles　of　camera　coordinate　system　relative　to　the　segment　coordinate　system　around　its　own　Xc　axis　and　Yc　axis　were　in　the　ranges　of(180.00±15.00)°　and　±20.00°,　respectively,　the　proposed　method　effectively　corrected　the　calculation　results　with　strong　robustness.　[Conclusions]　By　combining　deep　learning　algorithms　with　traditional　image　processing　techniques,　binocular　vision　technology　is　used　to　achieve　rapid　and　accurate　measurement　of　segment　dislocation.　This　approach　provides　a　reference　for　tunnel　engineering　inspection.　©　2025,　Tsinghua　University.　All　rights　reserved.