With　the　development　of　modern　weapons　such　as　UAV　swarms　and　multiwarhead　missiles,　infrared　(IR)　cluster　small　target　detection　technology　has　become　increasingly　important.　However,　the　difficulty　in　characterizing　cluster　multitargets　leads　to　poor　detection　performance　of　existing　methods.　On　the　one　hand,　this　letter　proposes　improved　DBSCAN　(IDBSCAN)　to　accurately　extract　the　features　of　cluster　multitargets　with　unknown　numbers　and　distribution.　On　the　other　hand,　an　IDBSCAN-based　difference　measure　(IDBSCAN-DM)　is　proposed,　which　fuses　saliency　and　distribution　features　to　further　enhance　cluster　multitargets.　Specifically,　we　first　design　the　multiscale　sliding　window　to　quickly　extract　candidate　targets.　Then,　the　IDBSCAN-based　local　window　is　constructed　and　IDBSCAN-DM　is　computed　for　better　target　enhancement　and　background　suppression.　Finally,　adaptive　threshold　segmentation　is　performed　on　the　IDBSCAN-DM　map　to　detect　real　targets.　Extensive　comparative　experiments　demonstrate　that　the　proposed　method　achieves　better　target　enhancement　and　a　higher　probability　of　detection.