In　this　paper,　a　dental　symptom　detection　model　based　on　YOLO　is　proposed　in　order　to　detect　different　dental　symptom　in　panoramic　oral　roentgenogram.　This　model　introduces　the　Global　Attention　Mechanism　into　the　backbone　feature　extraction　network　to　obtain　rich　cross-latitude　features　and　enhance　the　network＇s　global　feature　extraction　capabilities　in　low-contrast　images.　At　the　same　time,　the　Spatial　Pyramid　Pooling　Fast　module　in　the　network　is　replaced　and　the　Atrous　Spatial　Pyramid　Pooling　technology　is　used　to　improve　the　recognition　ability　of　larger　targets　such　as　tooth　germ.　Finally,　according　to　the　special　structure,　size　and　position　of　different　dental　symptoms,　the　Focal-EIoU　is　introduced　to　replace　CIoU,　which　increases　the　weight　proportion　of　positive　samples　in　the　training　process　and　reduces　the　problem　of　missed　detection　or　false　detection.　Experiments　on　self-built　data　sets　show　that　the　improved　YOLO　model　has　improved　mAP@0.5　by　4.3%　compared　to　the　original　model,　and　the　detection　effect　has　been　generally　improved.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.