To　address　the　increasing　issue　of　cracks　in　metro　shield　tunnels,　this　paper　proposes　the　YOLOv8-GSD　model,　which　integrates　DySnakeConv,　BiLevelRoutingAttention,　and　the　Gather-and-Distribute　Mechanism　with　the　YOLOv8　algorithm.　This　model　is　designed　for　detecting　and　segmenting　cracks　in　tunnel　linings　and　employs　a　pixel　grouping　method　to　measure　crack　length　and　width.　Using　a　real　crack　dataset　from　a　subway　section　in　Suzhou,　China,　comparative　experiments　with　YOLOv8x,　BlendMask,　SOLOv2,　and　YOLACT　demonstrate　that　YOLOv8-GSD　excels　in　segmentation　performance　(AP　of　82.4　%)　and　accuracy　(IoU　of　0.812).　The　measured　crack　dimensions　show　an　error　within　5　%　compared　to　actual　values,　confirming　the　model＇s　effectiveness.　These　results　highlight　the　potential　of　YOLOv8-GSD　for　enhancing　the　maintenance　and　safety　of　metro　tunnels.