Fire　is　a　destructive　disaster　that　threatens　human　safety　and　property.　Existing　fire　detection　algorithms　often　suffer　from　low　accuracy　and　high　omission　rates　due　to　varying　target　scales　and　complex　backgrounds.　Additionally,　many　models　are　computationally　complex,　making　them　unsuitable　for　real-time　deployment　on　edge　devices.　To　address　these　challenges,　this　paper　proposes　an　improved　YOLOv8　algorithm　based　on　knowledge　distillation,　aiming　to　efficiently　compress　the　model　while　maintaining　detection　accuracy.　To　enhance　the　model＇s　ability　to　focus　on　key　flame　features　and　improve　detection　accuracy,　we　introduce　a　Multi-scale　Spatial　Attention　(MSA)　mechanism.　To　address　the　challenge　of　feature　fusion　in　dense　fire　scenes,　we　design　the　Dense　Feature　Alignment　(DFA)　module,　which　refines　the　fusion　process　and　improves　adaptability.　Furthermore,　to　resolve　cross-task　inconsistencies　in　knowledge　distillation,　we　propose　the　Logic　Matching　(LM)　module,　ensuring　more　effective　knowledge　transfer　and　consistent　feature　representations　between　the　teacher　and　student　models.　Experimental　results　demonstrate　that　the　proposed　model　outperforms　existing　algorithms　in　fire　detection　accuracy.　Compared　to　the　baseline　model,　precision　and　recall　improved　by　4.9%　and　6.4%,　respectively,　while　mAP@0.5　increased　by　3.5%.　These　results　further　validate　the　model＇s　capability　for　real-time　fire　monitoring　and　accurate　fire　identification.　©　2025　IEEE.