In　addressing　the　challenge　of　distinguishing　between　regular　trucks　and　hazardous　transport　vehicles　within　the　park,　this　paper　introduces　an　enhanced　algorithm　for　hazardous　material　vehicle　detection,　leveraging　the　YOLOv7-tiny　model.　The　algorithm＇s　objective　is　to　swiftly　recognize　hazardous　material　markings　on　vehicles　and　differentiate　hazardous　material　transport　vehicles.　Initially,　a　dataset　comprising　8,000　images　and　10,000　hazardous　material　vehicle　samples　is　compiled.　Leveraging　prior　knowledge　of　hazardous　material　markings　enhances　the　detection　accuracy　of　hazardous　vehicles.　Subsequently,　the　YOLOv7-tiny　network　undergoes　several　enhancements:　the　CoT-ELAN　network　structure　is　proposed　to　improve　feature　extraction　for　small　targets　by　integrating　attention　mechanisms,　and　a　decoupled　head　structure　is　introduced　to　address　different　aspects　of　classification　and　localization,　thereby　boosting　overall　performance.　Experimental　findings　showcase　the　superior　performance　of　the　enhanced　algorithm　on　the　internally　constructed　dataset,　achieving　a　5.6%　increase　in　average　precision　compared　to　the　traditional　YOLOv7-tiny　model.　With　a　detection　speed　of　70.5　FPS　on　GPU　devices,　the　algorithm　effectively　meets　the　hazardous　material　vehicle　detection　needs　within　the　park.　©　2024　IEEE.