Object　detection　in　infrared　images　poses　a　considerable　challenge　due　to　its　small-scale　targets,　low　contrast　and　poor　signal-to-clutter　ratio,　often　resulting　in　a　high　false　alarm　rate.　To　improve　the　detection　accuracy　on　infrared　small　targets,　we　introduce　Light-SGMTLM,　a　lightweight　and　saliency-guided　multi-task　learning　model.　This　model　integrates　saliency　detection　into　the　YOLOv5x　framework　through　a　parallel　multi-task　learning　structure　and　employs　a　joint　loss　function　during　training.　Such　integration　significantly　alleviates　the　impact　of　complex　backgrounds　and　improves　the　precision　of　small　target　localization.　Moreover,　we　have　developed　a　streamlined　module,　termed　SIWD,　to　create　a　more　agile　backbone,　which　establishes　an　optimal　balance　between　precision　and　efficiency,　making　the　model　more　suitable　for　situations　with　limited　computational　resources.　Comprehensive　comparative　experiments　were　conducted　on　six　infrared　small　target　datasets,　namely,　Small-ExtIRShip,　Small-SSDD,　IHAST,　NUAA-SIRST,　IRSTD-1k,　and　IRDST,　and　we　assessed　the　model＇s　performance　against　ten　leading　target　detection　models,　such　as　YOLOv7,　YOLOv8,　DINO,　and　Relation-DETR.　The　findings　reveal　that　our　method＇s　unique　joint　learning　architecture,　combining　saliency　and　object　detection　tasks,　significantly　improves　accuracy　for　infrared　small　target　detection.　Notably,　it　achieved　impressive　mean　average　precision　(mAP)　values　of　92.60%　and　75.71%　on　the　NUAA-SIRST　and　IRSTD-1k　datasets,　respectively.