Deep　learning　technology　has　made　significant　advancements　in　object　detection,largely　due　to　the　availability　of　large-scale,accurately　annotated　datasets.　However,in　specific　fields　such　as　national　defense,maritime　security,and　medicine,obtaining　annotated　data　can　be　particularly　challenging　due　to　the　scarcity　of　samples.　As　a　result,few-shot　object　detection,which　aims　to　develop　algorithms　that　can　extract　knowledge　from　very　limited　samples　while　achieving　efficient　object　detection,has　garnered　substantial　attention　in　the　academic　community　for　its　potential　to　address　sample　sparsity.　One　of　the　main　challenges　in　few-shot　object　detection　is　the　significant　distribution　discrepancy　between　novel　and　base　classes,primarily　caused　by　the　limited　availability　of　novel　class　samples.　This　discrepancy　constrains　the　accuracy　of　detection　tasks.　Additionally,during　the　fine-tuning　process　for　novel　classes,the　non-overlapping　nature　of　novel　and　base　classes　often　leads　to　drastic　gradient　updates.　Consequently,as　the　model　learns　the　characteristics　of　novel　classes,it　may　forget　the　feature　knowledge　of　base　classes,resulting　in　a　decline　in　overall　performance.　To　tackle　the　issue　of　scarce　samples　for　novel　classes,this　study　employs　a　self-supervised　learning　strategy.　Self-supervised　learning　does　not　depend　on　annotated　information　and　allows　for　the　creation　of　proxy　tasks　that　facilitate　model　training,effectively　addressing　the　challenge　of　sample　scarcity　in　few-shot　object　detection.　To　mitigate　the　problem　of　catastrophic　forgetting　base　class　knowledge　after　acquiring　novel　class　features,this　paper　integrates　self-supervised　learning　with　a　two-stage　object　detector.　By　utilizing　latent　features　in　the　category　domain　to　represent　the　characteristics　of　each　class　and　implementing　dynamic　updating　strategies　to　further　refine　features　during　the　learning　process　of　new　classes,the　precision　of　the　regression　box　is　enhanced.　This　is　achieved　through　the　construction　of　well-designed　proxy　tasks　in　the　bounding　box　domain.　Extensive　experimental　validation　on　the　PASCAL　VOC　and　MS　COCO　datasets　demonstrated　that　the　proposed　method　outperforms　various　other　few-shot　object　detection　models,both　in　terms　of　novel　class　performance　and　overall　efficacy.　©　2025　Editorial　Board　of　Journal　of　Signal　Processing.　All　rights　reserved.