The　acceleration　of　global　warming　and　intensifying　global　climate　anomalies　have　led　to　a　rise　in　the　frequency　of　wildfires.　However,　most　existing　research　on　wildfire　fields　focuses　primarily　on　wildfire　identification　and　prediction,　with　limited　attention　given　to　the　intelligent　interpretation　of　detailed　information,　such　as　fire　front　within　fire　region.　To　address　this　gap,　advance　the　analysis　of　fire　front　in　UAV-captured　visible　images,　and　facilitate　future　calculations　of　fire　behavior　parameters,　a　new　method　is　proposed　for　the　intelligent　segmentation　and　fire　front　interpretation　of　wildfire　regions.　This　proposed　method　comprises　three　key　steps:　deep　learning-based　fire　segmentation,　boundary　tracking　of　wildfire　regions,　and　fire　front　interpretation.　Specifically,　the　YOLOv7-tiny　model　is　enhanced　with　a　Convolutional　Block　Attention　Module　(CBAM),　which　integrates　channel　and　spatial　attention　mechanisms　to　improve　the　model＇s　focus　on　wildfire　regions　and　boost　the　segmentation　precision.　Experimental　results　show　that　the　proposed　method　improved　detection　and　segmentation　precision　by　3.8　%　and　3.6　%,　respectively,　compared　to　existing　approaches,　and　achieved　an　average　segmentation　frame　rate　of　64.72　Hz,　which　is　well　above　the　30　Hz　threshold　required　for　real-time　fire　segmentation.　Furthermore,　the　method＇s　effectiveness　in　boundary　tracking　and　fire　front　interpreting　was　validated　using　an　outdoor　grassland　fire　fusion　experiment＇s　real　fire　image　data.　Additional　tests　were　conducted　in　southern　New　South　Wales,　Australia,　using　data　that　confirmed　the　robustness　of　the　method　in　accurately　interpreting　the　fire　front.　The　findings　of　this　research　have　potential　applications　in　dynamic　data-driven　forest　fire　spread　modeling　and　fire　digital　twinning　areas.　The　code　and　dataset　are　publicly　available　at　https://github.com/makemoneyokk/fire-segmentation-interpretation.git.