The　detection　of　unknown　objects　is　a　challenging　task　in　computer　vision　because,　although　there　are　diverse　real-world　detection　object　categories,　existing　object-detection　training　sets　cover　a　limited　number　of　object　categories　.　Most　existing　approaches　use　two-stage　networks　to　improve　a　model＇s　ability　to　characterize　objects　of　unknown　classes,　which　leads　to　slow　inference.　To　address　this　issue,　we　proposed　a　single-stage　unknown　object　detection　method　based　on　the　contrastive　language-image　pre-training　(CLIP)　model　and　pseudo-labelling,　called　CLIP-YOLO.　First,　a　visual　language　embedding　alignment　method　is　introduced　and　a　channel-grouped　enhanced　coordinate　attention　module　is　embedded　into　a　YOLO-series　detection　head　and　feature-enhancing　component,　to　improve　the　model＇s　ability　to　characterize　and　detect　unknown　category　objects.　Second,　the　pseudo-labelling　generation　is　optimized　based　on　the　CLIP　model　to　expand　the　diversity　of　the　training　set　and　enhance　the　ability　to　cover　unknown　object　categories.　We　validated　this　method　on　four　challenging　datasets:　MSCOCO,　ILSVRC,　Visual　Genome,　and　PASCAL　VOC.　The　results　show　that　our　method　can　achieve　higher　accuracy　and　faster　speed,　so　as　to　obtain　better　performance　of　unknown　object　detection.　The　source　code　is　available　at　https://github.com/BJUTsipl/CLIP-YOLO.