Fire　disaster　brings　enormous　danger　to　the　safety　of　human　life　and　property,　and　it　is　important　to　identify　the　fire　situation　in　time　through　image　processing　technology.　The　current　instance　segmentation　algorithms　suffer　from　problems　such　as　inadequate　fire　images　and　annotations,　low　recognition　accuracy,　and　slow　inference　speed　for　fire　recognition　tasks.　In　this　paper,　we　propose　a　semi-supervised　learning-based　fire　instance　segmentation　method　based　on　deep　learning　image　processing　technology.　We　used　a　lightweight　version　of　the　SOLOv2　network　and　optimized　the　network　structure　to　improve　accuracy.　We　propose　a　semi-supervised　learning　method　based　on　fire　features.　To　reduce　the　negative　impact　of　error　pseudo-labels　on　the　model　training,　the　pseudo-labels　are　matched　by　the　color　and　morphological　features　of　flames　and　smoke　at　the　pseudo-label　generation　stage,　and　some　images　are　screened　for　strong　image　enhancement　before　entering　the　next　round　of　training　for　the　student　model.　We　further　exploit　the　potential　of　the　model　with　a　limited　dataset　and　improve　the　model　accuracy　without　affecting　the　inference　efficiency　of　the　model.　Experiments　show　that　our　proposed　algorithm　can　successfully　improve　the　accuracy　of　fire　instance　segmentation　with　good　inference　speed.　©　2022　The　Author(s)