Civil　engineering　crack　detection　faces　challenges　due　to　complex　environments　and　external　interferences.　This　paper　proposes　an　improved　YOLO　v8s-WOMA　network,　integrating　ODConv,　C2f-MA　modules,　and　WIoU　loss　function　to　enhance　crack　identi　cation　accuracy.　A　BP　neural　network　is　also　trained　to　assess　crack　damage.　Experiments　on　the　CBP　dataset　compare　this　method　with　existing　detection　algorithms.　Results　show　that　the　proposed　model　achieves　the　highest　mAP　(90.5%),　F1-score　(90.3%),　and　accuracy　(89.6%).　Bridge　crack　detection　errors　remain　within　0.1mm　(width)　and　20mm　(length),　ensuring　precise　damage　assessment.　The　model　effectively　handles　complex　backgrounds,　accurately　detects　cracks,　and　meets　practical　engineering　needs.　©　2025　The　Author(s).　Published　by　Combinatorial　Press.　This　is　an　open　access　article　under　the　CC　BY　license.