Intelligently　identifying　cracks　in　metro　tunnels　is　challenging　due　to　factors　such　as　short　skylight　detection　time,　low　manual　detection　efficiency,　inconspicuous　crack　characteristics,　and　various　disturbances　in　the　tunnel.　Therefore,　a　modified　YOLO　(M-YOLO)　algorithm　based　on　YOLOv8　is　proposed　to　efficiently　and　intelligently　identify　cracks　in　tunnels.　This　algorithm　adopts　full-dimensional　dynamic　convolution　to　replace　the　traditional　convolution　module,　remarkably　improving　the　detection　accuracy　and　avoiding　model　parameter　expansion.　The　C2fGC　module　is　introduced　to　improve　the　network　structure,　establishing　a　new　feature　extraction　and　dimensionality　reduction　mechanism　and　enhancing　the　high-level　feature　representation.　The　convolutional　block　attention　module　attention　mechanism　module　is　introduced　to　strengthen　feature　learning　and　extraction　of　crack　region　images　and　reduce　background　interference,　effectively　improving　the　detection　accuracy.　The　WIOU　loss　function　is　introduced　to　adjust　the　degree　of　penalty　of　geometric　factors,　which　improves　the　generalization　ability　of　the　model　and　helps　it　perform　better　even　with　low-quality　data　samples.　The　test　results　show　that　in　the　real　samples,　which　are　used　for　identifying　cracks　in　a　metro　tunnel,　the　mean　accuracy　of　the　M-YOLO　algorithm　is　as　high　as　83.0%,　which　indicates　a　significant　improvement　of　15.7%　compared　with　the　original　model.　©　2024　Editorial　Office　of　Tunnel　Construction.　All　rights　reserved.