Infrared　(IR)　small　target　detection　is　a　challenging　task　due　to　the　low　contrast　and　low　signal-to-noise　ratio,　generally　yields　high　false　alarm　rates.　To　improve　the　performance　of　IR　small　target　detection,　we　propose　a　saliency　guided　multi-task　leaning　model　(SGMTLM).　The　model　consists　of　two　parts:　feature　fusion　and　saliency　detection.　The　feature　fusion　module　is　to　integrate　shallow　information　and　deep　semantic　information　of　small　targets.　The　saliency　detection　module　is　used　to　guide　the　Feature　Pyramid　Networks　(FPN)　to　focus　on　the　small　target　area.　It　can　effectively　suppress　the　non-target　information　while　enhancing　the　small　target　information.　Finally,　experimental　results　on　two　datasets　Small-　ExtIRShip　and　Small-SSDD　demonstrated　that,　with　the　help　of　saliency　detection,　the　proposed　method　can　effectively　improve　the　accuracy　of　IR　small　target　detection,　achieving　95.78%　and　98.70%　mAP　on　the　two　datasets,　respectively.